Methods for Assessing Risk of Developing Breast Cancer

ABSTRACT

The present disclosure relates to methods and systems for assessing the risk of a human female subject for developing breast cancer. In particular, the present disclosure relates to combining clinical risk assessment and genetic risk assessment to improve risk analysis.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of PCT International Application No. PCT/AU2015/050583, filed Sep. 29, 2015, which claims the priority of Australian Application No. 2014903898, filed Sep. 30, 2014, the contents of each of which are hereby incorporated by reference in their entirety into this application.

REFERENCE TO SEQUENCE LISTING

This application incorporates-by-reference nucleotide and/or amino acid sequences which are present in the file named “160419_88199_Sequence_Listing_DH.txt”, which is 3.08 kilobytes in size, and which was created Apr. 18, 2016 in the IBM-PC machine format, having an operating system compatibility with MS-Windows, which is contained in the text file filed Apr. 19, 2016 as part of this application.

TECHNICAL FIELD

The present disclosure relates to methods and systems for assessing the risk of a human female subject for developing a breast cancer. In particular, the present disclosure relates to combining clinical risk assessment and genetic risk assessment to improve risk analysis.

BACKGROUND OF THE INVENTION

It is estimated that in the USA approximately one in eight women will develop breast cancer in their lifetime. In 2013 it was predicted that over 230,000 women would be diagnosed with invasive breast cancer and almost 40,000 would die from the disease (ACS Breast Cancer Facts & FIGS. 2013-14). There is therefore a compelling reason to predict which women will develop disease, and to apply measures to prevent it.

A wide body of research has focused on phenotypic risk factors including age, family history, reproductive history, and benign breast disease. Various combinations of these risk factors have been compiled into the two most commonly used risk prediction algorithms; the Gail Model (appropriate for the general population) (also known as the Breast Cancer Risk Assessment Tool: BCRAT) and the Tyrer-Cuzick Model (appropriate for women with a stronger family history).

Breast cancer, like other common cancers, shows familial clustering. Numerous epidemiological studies have demonstrated that, the disease is approximately twice as common in first degree relatives of breast cancer patients. Family studies, and particularly twin studies, suggest that most if not all of this clustering has a genetic basis.

Several breast cancer susceptibility genes have already been identified, most importantly BRCA1 and BRCA2. Mutations in these genes confer a high risk of breast cancer (of the order of 65% and 45%, respectively, by age 70). Mutation screening of population-based series of breast cancer cases has shown that only about 15% of the familial risk of breast cancer can be explained by mutations in these genes. The other known breast cancer susceptibility genes (TP53, PTEN, ATM, CHEK2) make only small contributions to the familial risk (because the predisposing mutations are rare and/or confer only small risks). In total therefore, the known breast cancer susceptibility genes have been estimated to account for no more than 20% of the familial risk.

Genetic variation in risk may result from rare highly-penetrant mutations (such as those in BRCA1 and BRCA2) or from variants conferring more moderate risks. Several lines of evidence suggest strongly that high penetrance mutations are not major contributors to the residual familial risk of breast cancer. Firstly, mutation screening of multiple case families has found that the large majority of cases with a very strong family history (for example four or more affected relatives) harbor mutations in BRCA1 or BRCA2. Secondly, despite extensive efforts over the past nine years, genetic linkage studies have not identified any further linked loci. Thirdly, segregation analyses of large series of breast cancer families have found, after adjusting for BRCA1 and BRCA2, no evidence for a further major dominant breast cancer susceptibility allele.

Germline genetic testing for mutations in BRCA1 and BRCA2 is now routine in genetic medicine and allows for the identification of individuals at significantly increased risk for breast and other cancers. However, such mutations are relatively rare in the general population and account for approximately 10% of all breast cancer cases in the US (approximately half of which are due to BRCA1/2 mutations). The remaining 80% of sporadic breast cancers and those familial cancers for which no causative mutation is known have to be defined by other genetic/clinical markers common to the population at large.

The first commercially available test for assessing the risk of developing breast cancer which relies on the detection of low penetrance polymorphisms was the BREVAGen test described in WO 2010/139006. This test relies on the detection of 7 or 10 single nucleotide polymorphisms. However, there is the need for improved breast cancer risk assessment tests, particularly for non-Caucasian females.

SUMMARY OF THE INVENTION

The present inventors have identified SNP's within the genome that are useful for assessing the risk of a human female subject developing a breast cancer phenotype. Surprisingly, a selection of these SNP's remain informative across a plurality of ethnic backgrounds. These findings suggest that the SNP's of the present disclosure may be used in a method assessing the risk of a human female subject developing a breast cancer phenotype. In particular, these results suggest that such methods may be suitably robust to account for ethnic genotype variation.

Accordingly, in one aspect the present disclosure relates to a method for assessing the risk of a human female subject for developing a breast cancer comprising:

performing a clinical risk assessment of the female subject;

performing a genetic risk assessment of the female subject, wherein the genetic risk assessment involves detecting, in a biological sample derived from the female subject, at least 72 single nucleotide polymorphisms associated with a breast cancer, wherein at least 67 of the single nucleotide polymorphisms are selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof, and the remaining single nucleotide polymorphisms are selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof; and

combining the clinical risk assessment with the genetic risk assessment to obtain the risk of a human female subject for developing breast cancer.

One of skill in the art will appreciate that the combined clinical risk assessment and genetic risk assessment defines the subjects overall risk for developing a breast cancer. Thus, the methods of the invention assess overall risk.

In an embodiment, the methods of the present disclosure determine the absolute risk of a human female subject for developing breast cancer.

In another embodiment, the methods of the present disclosure determine the relative risk of a human female subject for developing breast cancer.

The female can be of any race such as Caucasian, Negroid, Australoid, or Mongoloid. In an embodiment, the female is post-menopausal.

In an embodiment, the female is Caucasian. In a further embodiment, the method comprises detecting at least 72 single nucleotide polymorphisms shown in Table 9, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an additional embodiment, the method comprises detecting at least the 77 single nucleotide polymorphisms shown in Table 9, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In another embodiment, the female is Negroid. In a further embodiment, the female is African-American. In a further embodiment, the method comprises detecting at least 74 single nucleotide polymorphisms shown in Table 10, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an additional embodiment, the method comprises detecting at least the 78 single nucleotide polymorphisms shown in Table 10, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In another embodiment, the female is Hispanic. In a further embodiment, the method of the present disclosure comprises detecting at least 78 single nucleotide polymorphisms shown in Table 11, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an additional embodiment, the method comprises detecting at least the 82 single nucleotide polymorphisms shown in Table 11, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, combining the clinical risk assessment with the genetic risk assessment comprises multiplying the risk assessments to provide the risk score.

In an embodiment, performing the clinical risk assessment uses a model selected from a group consisting of the Gail Model, the Claus Model, Claus Tables, BOADICEA, the Jonker Model, the Claus Extended Formula, the Tyrer-Cuzick Model, BRCAPRO, and the Manchester Scoring System.

In a further embodiment, performing the clinical risk assessment includes obtaining information from the female on one or more of the following: medical history of breast cancer, ductal carcinoma or lobular carcinoma, age, age of first menstrual period, age at which she first gave birth, family history of breast cancer, results of previous breast biopsies, breast density, and race/ethnicity.

In one embodiment, the clinical risk assessment is obtained using the Gail Model. In an embodiment, when the Gail Model is used, the subject is 35 years of age or older.

In an embodiment, the Gail Model provides a Gail Lifetime risk score.

In an embodiment, the Gail Model provides a Gail 5-year risk score.

In an alternate embodiment, the clinical risk assessment is obtained using the Tyrer-Cuzick Model.

In an embodiment, when the Tyrer-Cuzick Model is used, the subject is 20 years of age or older.

In an embodiment, the methods of the present disclosure comprise detecting at least 73, 74, 75, 76, 77, 78, 79, 80, 81, 82 single nucleotide polymorphisms associated with a breast cancer, wherein at least 67 of the single nucleotide polymorphisms are selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof and the remaining single nucleotide polymorphisms are selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the female has had a biopsy of the breast.

In an embodiment, the female has not had breast cancer, lobular carcinoma or ductal carcinoma.

In an embodiment, the results of the clinical risk assessment indicate that the female should be subjected to more frequent screening and/or prophylactic anti-breast cancer therapy.

In a further embodiment, if it is determined the subject has a risk of developing breast cancer, the subject is more likely to be responsive to oestrogen inhibition therapy than non-responsive.

In an embodiment, the breast cancer is estrogen receptive positive or estrogen receptor negative.

In an embodiment, a single nucleotide polymorphism in linkage disequilibrium has linkage disequilibrium above 0.9.

In another embodiment, a single nucleotide polymorphism in linkage disequilibrium has linkage disequilibrium of 1.

In an embodiment, the net reclassification improvement of the methods of the present disclosure is greater than 0.01.

In a further embodiment, the net reclassification improvement of the methods of the present disclosure is greater than 0.05.

In yet another embodiment, the net reclassification improvement of the methods of the present disclosure is greater than 0.1.

In another embodiment, the 5-year risk determined by the clinical risk assessment is between about 1.5% to about 2%.

In another aspect, the present disclosure relates to a kit comprising at least 72 sets of primers for amplifying 72 or more nucleic acids, wherein the 72 or more nucleic acids comprise a single nucleotide polymorphism, wherein at least 67 of the sets of primers amplify nucleic acids comprising a single nucleotide polymorphism selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof, and the remaining sets of primers amplify nucleic acids comprising a single nucleotide polymorphism selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In another aspect, the present disclosure relates to a genetic array comprising at least 72 sets of probes for hybridising to 72 or more nucleic acids, wherein the 72 or more nucleic acids comprise a single nucleotide polymorphism, wherein at least 67 of the probes hybridise to nucleic acids comprising a single nucleotide polymorphism selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof, and the remaining probes hybridise to nucleic acids comprising a single nucleotide polymorphism selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In another aspect, the present disclosure relates to a method for determining the need for routine diagnostic testing of a human female subject for breast cancer comprising assessing the risk of the subject for developing breast cancer using the disclosed methods.

Screening is recommended for women with an approximately 20-25% lifetime risk of breast cancer (Saslow et al., 2007). Thus, in an embodiment, a risk score greater than about 20% lifetime risk indicates that the subject should be enrolled in a screening breast MRIC and mammography program.

In another aspect, the present disclosure relates to a method of screening for breast cancer in a human female subject, the method comprising assessing the risk of the subject for developing breast cancer using the disclosed methods, and routinely screening for breast cancer in the subject if they are assessed as having a risk for developing breast cancer. For example, screening for breast cancer can involve enrolling the subject in a screening breast MRIC and mammography program.

In another aspect, the present disclosure relates to a method for determining the need of a human female subject for prophylactic anti-breast cancer therapy comprising assessing the risk of the subject for developing breast cancer using the disclosed methods.

Pharmacological intervention is recommended in women with a risk score greater than about 1.66% 5-year risk (Visvanathan et al., 2009). Thus, in an embodiment, an risk score greater than about 1.66% 5-year risk indicates that a chemopreventative should be offered to the subject. For example, estrogen receptor therapy could be offered to the subject. Various exemplary estrogen receptor therapies are discussed further below.

In another aspect, the present disclosure relates to a method for preventing breast cancer in a human female subject, the method comprising assessing the risk of the subject for developing a breast cancer using the disclosed methods, and administering an anti-breast cancer therapy to the subject if they are assessed as having a risk for developing breast cancer.

In one embodiment, the therapy inhibits oestrogen.

In a further aspect, the present disclosure relates to an anti-breast cancer therapy for use in preventing breast cancer in a human female subject at risk thereof, wherein the subject is assessed as having a risk for developing breast cancer according to the method of the present disclosure.

In another aspect, the present disclosure relates to a method for stratifying a group of human female subjects for a clinical trial of a candidate therapy, the method comprising assessing the individual risk of the subjects for developing breast cancer using the disclosed methods, and using the results of the assessment to select subjects more likely to be responsive to the therapy.

In another aspect, the present invention provides for the use of probes or at least 72 sets of primers for preparing a kit or system for assessing the risk of a human female subject for developing a breast cancer phenotype comprising:

performing a clinical risk assessment of the female subject;

performing a genetic risk assessment of the female subject, wherein the genetic risk assessment involves detecting, in a biological sample derived from the female subject, at least 72 single nucleotide polymorphisms associated with a breast cancer, wherein at least 67 of the single nucleotide polymorphisms are selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof, and the remaining single nucleotide polymorphisms are selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof; and

combining the clinical risk assessment with the genetic risk assessment to obtain the risk of a human female subject for developing breast cancer.

In another aspect, the present disclosure relates to a computer implemented method for assessing the risk of a human female subject for developing breast cancer, the method operable in a computing system comprising a processor and a memory, the method comprising:

receiving clinical risk data and genetic risk data for the female subject, wherein the genetic risk data was obtained by detecting, in a biological sample derived from the female subject, at least 72 single nucleotide polymorphisms associated with breast cancer, wherein at least 67 of the single nucleotide polymorphisms are selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof, and the remaining single nucleotide polymorphisms are selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof;

processing the data to combine the clinical risk data with the genetic risk data to obtain the risk of a human female subject for developing breast cancer;

outputting the risk of a human female subject for developing breast cancer.

In one embodiment, the clinical risk data and genetic risk data for the female subject is received from a user interface coupled to the computing system.

In another embodiment, the clinical risk data and genetic risk data for the female subject is received from a remote device across a wireless communications network.

In another embodiment, outputting comprises outputting information to a user interface coupled to the computing system.

In another embodiment, outputting comprises transmitting information to a remote device across a wireless communications network.

In another embodiment, the present disclosure relates to a system configured to perform the disclosed methods.

In another embodiment, the present disclosure relates to a system for assessing the risk of a human female subject for developing breast cancer comprising:

system instructions for performing a clinical risk assessment of the female subject;

system instructions for performing a genetic risk assessment of the female subject according to the present disclosure; and

system instructions for combining the clinical risk assessment with the genetic risk assessment to obtain the risk of a human female subject for developing breast cancer.

As will be apparent, at least some features of the methods, kits and systems can be used together in combination. For example, systems for identifying correlations between breast cancer susceptibility and polymorphisms can be used for practicing the methods herein. Kits can be used for practicing the methods herein. Thus, described features of the systems, methods and kits can be applied to the different systems, methods and kits herein.

Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

The invention is hereinafter described by way of the following non-limiting Examples and with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

FIG. 1: depicts patients integrated lifetime risk.

FIG. 2: depicts patients integrated 5 year risk.

DETAILED DESCRIPTION OF THE INVENTION General Techniques and Definitions

Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., in cell culture, breast cancer analysis, molecular genetics, immunology, immunohistochemistry, protein chemistry, and biochemistry).

Unless otherwise indicated, the molecular, and immunological techniques utilized in the present disclosure are standard procedures, well known to those skilled in the art. Such techniques are described and explained throughout the literature in sources such as, J. Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons (1984), J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbour Laboratory Press (1989), T. A. Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press (1991), D. M. Glover and B. D. Hames (editors), DNA Cloning: A Practical Approach, Volumes 1-4, IRL Press (1995 and 1996), and F. M. Ausubel et al. (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience (1988, including all updates until present), Ed Harlow and David Lane (editors) Antibodies: A Laboratory Manual, Cold Spring Harbour Laboratory, (1988), and J. E. Coligan et al. (editors) Current Protocols in Immunology, John Wiley & Sons (including all updates until present).

It is to be understood that this disclosure is not limited to particular embodiments, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. As used in this specification and the appended claims, terms in the singular and the singular forms “a,” “an” and “the,” for example, optionally include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to “a probe” optionally includes a plurality of probe molecules; similarly, depending on the context, use of the term “a nucleic acid” optionally includes, as a practical matter, many copies of that nucleic acid molecule.

As used herein, the term “about”, unless stated to the contrary, refers to +/−10%, more preferably +/−5%, more preferably +/−1%, of the designated value.

As used herein, the term “breast cancer” encompasses any type of breast cancer that can develop in a female subject. For example, the breast cancer may be characterised as Luminal A (ER+ and/or PR+, HER2−, low Ki67), Luminal B (ER+ and/or PR+, HER2+ (or HER2− with high Ki67), Triple negative/basal-like (ER−, PR−, HER2−) or HER2 type (ER−, PR−, HER2+). In another example, the breast cancer may be resistant to therapy or therapies such as alkylating agents, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, endocrine/hormonal agents, bisphophonate therapy agents or targeted biological therapy agents. As used herein, “breast cancer” also encompasses a phenotype that displays a predisposition towards developing breast cancer in an individual. A phenotype that displays a predisposition for breast cancer, can, for example, show a higher likelihood that the cancer will develop in an individual with the phenotype than in members of a relevant general population under a given set of environmental conditions (diet, physical activity regime, geographic location, etc.).

As used herein, “biological sample” refers to any sample comprising nucleic acids, especially DNA, from or derived from a human patient, e.g., bodily fluids (blood, saliva, urine etc.), biopsy, tissue, and/or waste from the patient. Thus, tissue biopsies, stool, sputum, saliva, blood, lymph, or the like can easily be screened for SNPs, as can essentially any tissue of interest that contains the appropriate nucleic acids. In one embodiment, the biological sample is a cheek cell sample. These samples are typically taken, following informed consent, from a patient by standard medical laboratory methods. The sample may be in a form taken directly from the patient, or may be at least partially processed (purified) to remove at least some non-nucleic acid material.

A “polymorphism” is a locus that is variable; that is, within a population, the nucleotide sequence at a polymorphism has more than one version or allele. One example of a polymorphism is a “single nucleotide polymorphism”, which is a polymorphism at a single nucleotide position in a genome (the nucleotide at the specified position varies between individuals or populations).

As used herein, the term “SNP” or “single nucleotide polymorphism” refers to a genetic variation between individuals; e.g., a single nitrogenous base position in the DNA of organisms that is variable. As used herein, “SNPs” is the plural of SNP. Of course, when one refers to DNA herein, such reference may include derivatives of the DNA such as amplicons, RNA transcripts thereof, etc.

The term “allele” refers to one of two or more different nucleotide sequences that occur or are encoded at a specific locus, or two or more different polypeptide sequences encoded by such a locus. For example, a first allele can occur on one chromosome, while a second allele occurs on a second homologous chromosome, e.g., as occurs for different chromosomes of a heterozygous individual, or between different homozygous or heterozygous individuals in a population. An allele “positively” correlates with a trait when it is linked to it and when presence of the allele is an indicator that the trait or trait form will occur in an individual comprising the allele. An allele “negatively” correlates with a trait when it is linked to it and when presence of the allele is an indicator that a trait or trait form will not occur in an individual comprising the allele.

A marker polymorphism or allele is “correlated” or “associated” with a specified phenotype (breast cancer susceptibility, etc.) when it can be statistically linked (positively or negatively) to the phenotype. Methods for determining whether a polymorphism or allele is statistically linked are known to those in the art. That is, the specified polymorphism occurs more commonly in a case population (e.g., breast cancer patients) than in a control population (e.g., individuals that do not have breast cancer). This correlation is often inferred as being causal in nature, but it need not be, simple genetic linkage to (association with) a locus for a trait that underlies the phenotype is sufficient for correlation/association to occur.

The phrase “linkage disequilibrium” (LD) is used to describe the statistical correlation between two neighbouring polymorphic genotypes. Typically, LD refers to the correlation between the alleles of a random gamete at the two loci, assuming Hardy-Weinberg equilibrium (statistical independence) between gametes. LD is quantified with either Lewontin's parameter of association (D′) or with Pearson correlation coefficient (r) (Devlin and Risch, 1995). Two loci with a LD value of 1 are said to be in complete LD. At the other extreme, two loci with a LD value of 0 are termed to be in linkage equilibrium. Linkage disequilibrium is calculated following the application of the expectation maximization algorithm (EM) for the estimation of haplotype frequencies (Slatkin and Excoffier, 1996). LD values according to the present disclosure for neighbouring genotypes/loci are selected above 0.1, preferably, above 0.2, more preferable above 0.5, more preferably, above 0.6, still more preferably, above 0.7, preferably, above 0.8, more preferably above 0.9, ideally about 1.0.

Another way one of skill in the art can readily identify SNPs in linkage disequilibrium with the SNPs of the present disclosure is determining the LOD score for two loci. LOD stands for “logarithm of the odds”, a statistical estimate of whether two genes, or a gene and a disease gene, are likely to be located near each other on a chromosome and are therefore likely to be inherited. A LOD score of between about 2-3 or higher is generally understood to mean that two genes are located close to each other on the chromosome. Various examples of SNPs in linkage disequilibrium with the SNPs of the present disclosure are shown in Tables 1 to 4. The present inventors have found that many of the SNPs in linkage disequilibrium with the SNPs of the present disclosure have a LOD score of between about 2-50. Accordingly, in an embodiment, LOD values according to the present disclosure for neighbouring genotypes/loci are selected at least above 2, at least above 3, at least above 4, at least above 5, at least above 6, at least above 7, at least above 8, at least above 9, at least above 10, at least above 20 at least above 30, at least above 40, at least above 50.

In another embodiment, SNPs in linkage disequilibrium with the SNPs of the present disclosure can have a specified genetic recombination distance of less than or equal to about 20 centimorgan (cM) or less. For example, 15 cM or less, 10 cM or less, 9 cM or less, 8 cM or less, 7 cM or less, 6 cM or less, 5 cM or less, 4 cM or less, 3 cM or less, 2 cM or less, 1 cM or less, 0.75 cM or less, 0.5 cM or less, 0.25 cM or less, or 0.1 cM or less. For example, two linked loci within a single chromosome segment can undergo recombination during meiosis with each other at a frequency of less than or equal to about 20%, about 19%, about 18%, about 17%, about 16%, about 15%, about 14%, about 13%, about 12%, about 11%, about 10%, about 9%, about 8%, about 7%, about 6%, about 5%, about 4%, about 3%, about 2%, about 1%, about 0.75%, about 0.5%, about 0.25%, or about 0.1% or less.

In another embodiment, SNPs in linkage disequilibrium with the SNPs of the present disclosure are within at least 100 kb (which correlates in humans to about 0.1 cM, depending on local recombination rate), at least 50 kb, at least 20 kb or less of each other.

For example, one approach for the identification of surrogate markers for a particular SNP involves a simple strategy that presumes that SNPs surrounding the target SNP are in linkage disequilibrium and can therefore provide information about disease susceptibility. Thus, as described herein, surrogate markers can therefore be identified from publicly available databases, such as HAPMAP, by searching for SNPs fulfilling certain criteria which have been found in the scientific community to be suitable for the selection of surrogate marker candidates (see, for example, the legends of Tables 1 to 4).

“Allele frequency” refers to the frequency (proportion or percentage) at which an allele is present at a locus within an individual, within a line or within a population of lines. For example, for an allele “A,” diploid individuals of genotype “AA,” “Aa,” or “aa” have allele frequencies of 1.0, 0.5, or 0.0, respectively. One can estimate the allele frequency within a line or population (e.g., cases or controls) by averaging the allele frequencies of a sample of individuals from that line or population. Similarly, one can calculate the allele frequency within a population of lines by averaging the allele frequencies of lines that make up the population.

In an embodiment, the term “allele frequency” is used to define the minor allele frequency (MAF). MAF refers to the frequency at which the least common allele occurs in a given population.

An individual is “homozygous” if the individual has only one type of allele at a given locus (e.g., a diploid individual has a copy of the same allele at a locus for each of two homologous chromosomes). An individual is “heterozygous” if more than one allele type is present at a given locus (e.g., a diploid individual with one copy each of two different alleles). The term “homogeneity” indicates that members of a group have the same genotype at one or more specific loci. In contrast, the term “heterogeneity” is used to indicate that individuals within the group differ in genotype at one or more specific loci.

A “locus” is a chromosomal position or region. For example, a polymorphic locus is a position or region where a polymorphic nucleic acid, trait determinant, gene or marker is located. In a further example, a “gene locus” is a specific chromosome location (region) in the genome of a species where a specific gene can be found.

A “marker,” “molecular marker” or “marker nucleic acid” refers to a nucleotide sequence or encoded product thereof (e.g., a protein) used as a point of reference when identifying a locus or a linked locus. A marker can be derived from genomic nucleotide sequence or from expressed nucleotide sequences (e.g., from an RNA, nRNA, mRNA, a cDNA, etc.), or from an encoded polypeptide. The term also refers to nucleic acid sequences complementary to or flanking the marker sequences, such as nucleic acids used as probes or primer pairs capable of amplifying the marker sequence. A “marker probe” is a nucleic acid sequence or molecule that can be used to identify the presence of a marker locus, e.g., a nucleic acid probe that is complementary to a marker locus sequence. Nucleic acids are “complementary” when they specifically hybridize in solution, e.g., according to Watson-Crick base pairing rules. A “marker locus” is a locus that can be used to track the presence of a second linked locus, e.g., a linked or correlated locus that encodes or contributes to the population variation of a phenotypic trait. For example, a marker locus can be used to monitor segregation of alleles at a locus, such as a QTL, that are genetically or physically linked to the marker locus. Thus, a “marker allele,” alternatively an “allele of a marker locus” is one of a plurality of polymorphic nucleotide sequences found at a marker locus in a population that is polymorphic for the marker locus. Each of the identified markers is expected to be in close physical and genetic proximity (resulting in physical and/or genetic linkage) to a genetic element, e.g., a QTL, that contributes to the relevant phenotype. Markers corresponding to genetic polymorphisms between members of a population can be detected by methods well-established in the art. These include, e.g., PCR-based sequence specific amplification methods, detection of restriction fragment length polymorphisms (RFLP), detection of isozyme markers, detection of allele specific hybridization (ASH), detection of single nucleotide extension, detection of amplified variable sequences of the genome, detection of self-sustained sequence replication, detection of simple sequence repeats (SSRs), detection of single nucleotide polymorphisms (SNPs), or detection of amplified fragment length polymorphisms (AFLPs).

The term “amplifying” in the context of nucleic acid amplification is any process whereby additional copies of a selected nucleic acid (or a transcribed form thereof) are produced. Typical amplification methods include various polymerase based replication methods, including the polymerase chain reaction (PCR), ligase mediated methods such as the ligase chain reaction (LCR) and RNA polymerase based amplification (e.g., by transcription) methods.

An “amplicon” is an amplified nucleic acid, e.g., a nucleic acid that is produced by amplifying a template nucleic acid by any available amplification method (e.g., PCR, LCR, transcription, or the like).

A “gene” is one or more sequence(s) of nucleotides in a genome that together encode one or more expressed molecules, e.g., an RNA, or polypeptide. The gene can include coding sequences that are transcribed into RNA which may then be translated into a polypeptide sequence, and can include associated structural or regulatory sequences that aid in replication or expression of the gene.

A “genotype” is the genetic constitution of an individual (or group of individuals) at one or more genetic loci. Genotype is defined by the allele(s) of one or more known loci of the individual, typically, the compilation of alleles inherited from its parents.

A “haplotype” is the genotype of an individual at a plurality of genetic loci on a single DNA strand. Typically, the genetic loci described by a haplotype are physically and genetically linked, i.e., on the same chromosome strand.

A “set” of markers, probes or primers refers to a collection or group of markers probes, primers, or the data derived therefrom, used for a common purpose, e.g., identifying an individual with a specified genotype (e.g., risk of developing breast cancer). Frequently, data corresponding to the markers, probes or primers, or derived from their use, is stored in an electronic medium. While each of the members of a set possess utility with respect to the specified purpose, individual markers selected from the set as well as subsets including some, but not all of the markers, are also effective in achieving the specified purpose.

The polymorphisms and genes, and corresponding marker probes, amplicons or primers described above can be embodied in any system herein, either in the form of physical nucleic acids, or in the form of system instructions that include sequence information for the nucleic acids. For example, the system can include primers or amplicons corresponding to (or that amplify a portion of) a gene or polymorphism described herein. As in the methods above, the set of marker probes or primers optionally detects a plurality of polymorphisms in a plurality of said genes or genetic loci. Thus, for example, the set of marker probes or primers detects at least one polymorphism in each of these polymorphisms or genes, or any other polymorphism, gene or locus defined herein. Any such probe or primer can include a nucleotide sequence of any such polymorphism or gene, or a complementary nucleic acid thereof, or a transcribed product thereof (e.g., a nRNA or mRNA form produced from a genomic sequence, e.g., by transcription or splicing).

As used herein, “Receiver operating characteristic curves” refer to a graphical plot of the sensitivity vs. (1−specificity) for a binary classifier system as its discrimination threshold is varied. The ROC can also be represented equivalently by plotting the fraction of true positives (TPR=true positive rate) vs. the fraction of false positives (FPR=false positive rate). Also known as a Relative Operating Characteristic curve, because it is a comparison of two operating characteristics (TPR & FPR) as the criterion changes. ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. Methods of using in the context of the disclosure will be clear to those skilled in the art.

As used herein, the term “combining the clinical risk assessment with the genetic risk assessment to obtain the risk” refers to any suitable mathematical analysis relying on the results of the two assessments. For example, the results of the clinical risk assessment and the genetic risk assessment may be added, more preferably multiplied.

As used herein, the terms “routinely screening for breast cancer” and “more frequent screening” are relative terms, and are based on a comparison to the level of screening recommended to a subject who has no identified risk of developing breast cancer.

Ethnic Genotype Variation

It is known to those of skill in the art that genotypic variation exists between different populations. This phenomenon is referred to as human genetic variation. Human genetic variation is often observed between populations from different ethnic backgrounds. Such variation is rarely consistent and is often directed by various combinations of environmental and lifestyle factors. As a result of genetic variation, it is often difficult to identify a population of genetic markers such as SNPs that remain informative across various populations such as populations from different ethnic backgrounds.

Surprisingly, the present inventors have identified a selection of SNPs that are common to at least three ethnic backgrounds that remain informative for assessing the risk for developing breast cancer.

Accordingly, it is envisaged that the methods of the present disclosure can be used for assessing the risk for developing breast cancer in human female subjects from various ethnic backgrounds. For example, the female can be classified as Caucasoid, Australoid, Mongoloid and Negroid based on physical anthropology.

In an embodiment, the human female subject can be Caucasian, African American, Hispanic, Asian, Indian, or Latino. In a preferred embodiment, the human female subject is Caucasian, African American or Hispanic.

In one embodiment, the human female subject is Caucasian and at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, single nucleotide polymorphisms selected from Table 9, or a single nucleotide polymorphism in linkage disequilibrium therewith are assessed. Alternatively, at least 77 single nucleotide polymorphisms selected from Table 9 or a single nucleotide polymorphism in linkage disequilibrium therewith are assessed.

In another embodiment, the human female subject can be Negroid and at least 74, at least 75, at least 76, at least 77, at least 78, single nucleotide polymorphisms selected from Table 10, or a single nucleotide polymorphism in linkage disequilibrium therewith are assessed. Alternatively, at least 78 single nucleotide polymorphisms selected from Table 10 or a single nucleotide polymorphism in linkage disequilibrium therewith are assessed.

In another embodiment, the human female subject can be African American and at least 74, at least 75, at least 76, at least 77, at least 78, single nucleotide polymorphisms selected from Table 10, or a single nucleotide polymorphism in linkage disequilibrium therewith are assessed. Alternatively, at least 78 single nucleotide polymorphisms selected from Table 10 or a single nucleotide polymorphism in linkage disequilibrium therewith are assessed.

In a further embodiment, the human female subject can be Hispanic and at least 78, at least 79, at least 80, at least 81, at least 82, single nucleotide polymorphisms selected from Table 11, or a single nucleotide polymorphism in linkage disequilibrium therewith are assessed. Alternatively, at least 82 single nucleotide polymorphisms selected from Table 11 or a single nucleotide polymorphism in linkage disequilibrium therewith are assessed.

It is well known that over time there has been blending of different ethnic origins. However, in practice this does not influence the ability of a skilled person to practice the invention.

A female of predominantly European origin, either direct or indirect through ancestry, with white skin is considered Caucasian in the context of the present disclosure. A Caucasian may have, for example, at least 75% Caucasian ancestry (for example, but not limited to, the female having at least three Caucasian grandparents).

A female of predominantly central or southern African origin, either direct or indirect through ancestry, is considered Negroid in the context of the present disclosure. A Negroid may have, for example, at least 75% Negroid ancestry. An American female with predominantly Negroid ancestry and black skin is considered African American in the context of the present disclosure. An African American may have, for example, at least 75% Negroid ancestry. Similar principle applies to, for example, females of Negroid ancestry living in other countries (for example Great Britain, Canada of The Netherlands)

A female predominantly originating from Spain or a Spanish-speaking country, such as a country of Central or Southern America, either direct or indirect through ancestry, is considered Hispanic in the context of the present disclosure. An Hispanic may have, for example, at least 75% Hispanic ancestry.

The present inventors have found that the invention can readily be practiced based on what race/ancestry the subject considers themselves to be. Thus, in an embodiment, the ethnicity of the human female subject is self-reported by the subject. As an example, female subjects can be asked to identify their ethnicity in response to this question: “To what ethnic group do you belong?”

In another example, the ethnicity of the female subject is derived from medical records after obtaining the appropriate consent from the subject or from the opinion or observations of a clinician.

Naturally, in cases where there is no predominant ancestry, for example 50% Caucasian and 50% Negroid, the invention can still be practiced by focussing on the common polymorphisms provided in Table 7.

Clinical Risk Assessment

Any suitable clinical risk assessment procedure can be used in the present disclosure. Preferably, the clinical risk assessment does not involve genotyping the female at one or more loci.

In an embodiment, the clinical risk assessment procedure includes obtaining information from the female on one or more of the following: medical history of breast cancer, ductal carcinoma or lobular carcinoma, age, menstrual history such as age of first menstrual period, age at which she first gave birth, family history of breast cancer or other cancer including the age of the relative at the time of diagnosis, results of previous breast biopsies, use of oral contraceptives, body mass index, alcohol consumption history, smoking history, exercise history, diet and race/ethnicity.

In an embodiment, the clinical risk assessment at least takes into consideration the age, number of previous breast biopsies and known history among first degree relatives.

In an embodiment the clinical risk assessment procedure provides an estimate of the risk of the human female subject developing breast cancer during the next 5-year period (i.e. 5-year risk).

In an embodiment the 5-year risk determined by the clinical risk assessment is between about 1% to about 3%.

In another embodiment the 5-year risk determined by the clinical risk assessment is between about 1.5% to about 2%.

In an embodiment the clinical risk assessment procedure provides an estimate of the risk of the human female subject developing breast cancer up to age 90 (i.e. lifetime risk).

In an embodiment the lifetime risk determined by the clinical risk assessment is between about 15% to about 30%.

In another embodiment the 5-year risk determined by the clinical risk assessment is between about 20% to about 25%.

Examples of clinical risk assessment procedures include, but are not limited to, the Gail Model (BCRAT) (Gail et al., 1989, 1999 and 2007; Costantino et al., 1999; Rockhill et al., 2001), the Claus model (Claus et al., 1994 and 1998), Claus Tables, BOADICEA (Antoniou et al., 2002 and 2004), BRCAPRO (Parmigiani et al., 2007), the Jonker Model (Jonker et al., 2003), the Claus Extended Formula (van Asperen et al., 2004), the Tyrer-Cuzick Model (Tyrer et al., 2004), the Manchester Scoring System (Evans et al., 2004) and the like.

In an example, the clinical risk assessment procedure is the Gail Model. Such procedures can be used to estimate the 5-year risk or lifetime risk of a human female subject. The Gail Model is a statistical model which forms the basis of a breast cancer risk assessment tool, named after Dr. Mitchell Gail, Senior Investigator in the Biostatistics Branch of NCI's Division of Cancer Epidemiology and Genetics. The model uses a woman's own personal medical history (number of previous breast biopsies and the presence of atypical hyperplasia in any previous breast biopsy specimen), her own reproductive history (age at the start of menstruation and age at the first live birth of a child), and the history of breast cancer among her first-degree relatives (mother, sisters, daughters) to estimate her risk of developing invasive breast cancer over specific periods of time. Data from the Breast Cancer Detection Demonstration Project (BCDDP), which was a joint NCI and American Cancer Society breast cancer screening study that involved 280,000 women aged 35 to 74 years, and from NCI's Surveillance, Epidemiology, and End Results (SEER) Program were used in developing the model. Estimates for African American women were based on data from the Women's Contraceptive and Reproductive Experiences (CARE) Study and from SEER data. CARE participants included 1,607 women with invasive breast cancer and 1,637 without.

The Gail model has been tested in large populations of white women and has been shown to provide accurate estimates of breast cancer risk. In other words, the model has been “validated” for white women. It has also been tested in data from the Women's Health Initiative for African American women, and the model performs well, but may underestimate risk in African American women with previous biopsies. The model has also been validated for Hispanic women, Asian American women and Native American women.

In another example, the clinical risk assessment procedure is the Tyrer-Cuzick model. The Tyrer-Cuzick model incorporates both genetic and non-genetic factors (Tyrer et al., 2004). Nonetheless, the Tyrer-Cuzick model is considered separate from the genetic risk assessment outlined in the present disclosure. The Tyrer-Cuzick uses a three-generation pedigree to estimate the likelihood that an individual carries either a BRCA1/BRCA2 mutation or a hypothetical low-penetrance gene. In addition, the model incorporates personal risk factors, such as parity, body mass index, height, and age at menarche, menopause, HRT use, and first live birth.

In another example, the clinical risk assessment procedure is the BOADICEA model. The BOADICEA model was designed with the use of segregation analysis in which susceptibility is explained by mutations in BRCA1 and BRCA2 as well as a polygenic component that reflects the multiplicative effect of multiple genes, which individually have small effects on breast cancer risk (Antoniou et al., 2002 and 2004). This algorithm allows for prediction of BRCA1/BRCA2 mutation probabilities and for cancer risk estimation in individuals with a family history of breast cancer.

In another example, the clinical risk assessment procedure is the BRCAPRO model. The BRCAPRO Model is a Bayesian model that incorporates published BRCA1 and BRCA2 mutation frequencies. Cancer penetrance in mutation carriers, cancer status (affected, unaffected, unknown) and age of the patient's first-degree and second degree relatives (Parmigiani et al., 1998). This algorithm allows for prediction of BRCA1/BRCA2 mutation probabilities and for cancer risk estimation in individuals with a family history of breast cancer.

In another example, the clinical risk assessment procedure is the Claus model. The Claus Model provides an assessment of hereditary risk of developing breast cancer. The model was developed using data from the Cancer and Steroid Hormone Study. The model originally only included data on family history of breast cancer (Claus et al., 1991), but was later updated to include data on family history of ovarian cancer (Claus et al., 1993). In practice, lifetime risk estimates are usually derived from so-called Claus Tables (Claus et al., 1994). The model was further modified to incorporate information on bilateral disease, ovarian cancer, and three or more affected relatives and termed the “Claus Extended Model” (van Asperen et al., 2004).

Genetic Risk Assessment

In one aspect, the methods of the present disclosure relate to assessing the risk of a female subject for developing breast cancer by performing a genetic risk assessment. In another aspect, these methods can also incorporate a clinical risk assessment to provide a combined risk for developing breast cancer.

The genetic risk assessment is performed by analysing the genotype of the subject at 72 or more loci for single nucleotide polymorphisms associated with breast cancer. As the skilled addressee will appreciate, each SNP which increases the risk of developing breast cancer has an odds ratio of association with breast cancer of greater than 1.0, more preferably greater than 1.02. Examples of such SNPs include, but are not limited to, those provided in Tables 6 to 11, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

As the skilled addressee will appreciate, each SNP which decreases the risk of developing breast cancer has an odds ratio of association with breast cancer of less than 1.0. In an embodiment, the odds ratio is less than 0.98.

In an embodiment, when performing the methods of the present disclosure at least 67 of the single nucleotide polymorphisms are selected from Table 7 or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof and the remaining single nucleotide polymorphisms are selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof. In another embodiment, when performing the methods of the present disclosure at least 68, at least 69, at least 70 of the single nucleotide polymorphisms are selected from Table 7 or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof and the remaining single nucleotide polymorphisms are selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

SNPs in linkage disequilibrium with those specifically mentioned herein are easily identified by those of skill in the art. Examples of such SNPs include rs1219648 and rs2420946 which are in strong linkage disequilibrium with rs2981582 (further possible examples provided in Table 1), rs12443621 and rs8051542 which are in strong linkage disequilibrium with SNP rs3803662 (further possible examples provided in Table 2), and rs10941679 which is in strong linkage disequilibrium with SNP rs4415084 (further possible examples provided in Table 3). In addition, examples of SNPs in linkage disequilibrium with rs13387042 provided in Table 4. Such linked polymorphisms for the other SNPs listed in Table 6 can very easily be identified by the skilled person using the HAPMAP database.

In one embodiment, at least 72, at least 73, at least 74, at least 75, at least 76, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88 of single nucleotide polymorphisms shown in Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof are assessed. In further embodiments, at least 67, at least 68, at least 69, at least 70, shown in Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof are assessed.

In further embodiments, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88 single nucleotide polymorphisms are assessed, wherein at least 67, at least 68, at least 69, at least 70, shown in Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof are assessed, with any remaining SNPs being selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

TABLE 1 Surrogate markers for SNP rs2981582. Markers with a r2 greater than 0.05 to rs2981582 in the HAPMAP dataset (http://hapmap.ncbi.nlm.nih.gov) in a 1 Mbp interval flanking the marker was selected. Shown is the name of the correlated SNP, values for r2 and D′ to rs2981582 and the corresponding LOD value, as well as the position of the surrogate marker in NCB Build 36. DbSNP Correlated rsID Position SNP Location D′ r² LOD rs2981582 123342307 rs3135715 123344716 1.000 0.368 15.02 rs2981582 123342307 rs7899765 123345678 1.000 0.053  2.44 rs2981582 123342307 rs1047111 123347551 0.938 0.226  9.11 rs2981582 123342307 rs1219639 123348302 1.000 0.143  6.53 rs2981582 123342307 rs10886955 123360344 0.908 0.131  5.42 rs2981582 123342307 rs1631281 123380775 0.906 0.124  5.33 rs2981582 123342307 rs3104685 123381354 0.896 0.108  4.58 rs2981582 123342307 rs1909670 123386718 1.000 0.135  6.12 rs2981582 123342307 rs7917459 123392364 1.000 0.135  6.42 rs2981582 123342307 rs17102382 123393846 1.000 0.135  6.42 rs2981582 123342307 rs10788196 123407625 1.000 0.202  9.18 rs2981582 123342307 rs2935717 123426236 0.926 0.165  7.30 rs2981582 123342307 rs3104688 123426455 0.820 0.051  2.07 rs2981582 123342307 rs4752578 123426514 1.000 0.106  5.15 rs2981582 123342307 rs1696803 123426940 0.926 0.168  7.33 rs2981582 123342307 rs12262574 123428112 1.000 0.143  7.39 rs2981582 123342307 rs4752579 123431182 1.000 0.106  5.15 rs2981582 123342307 rs12358208 123460953 0.761 0.077  2.46 rs2981582 123342307 rs17102484 123462020 0.758 0.065  2.39 rs2981582 123342307 rs2936859 123469277 0.260 0.052  1.56 rs2981582 123342307 rs10160140 123541979 0.590 0.016  0.40

TABLE 2 Surrogate markers for SNP rs3803662. Markers with a r2 greater than 0.05 to rs3803662 in the HAPMAP dataset (http://hapmap.ncbi.nlm.nih.gov) in a 1 Mbp interval flanking the marker was selected. Shown is the name of the correlated SNP, values for r2 and D′ to rs3803662 and the corresponding LOD value, as well as the position of the surrogate marker in NCB Build 36. DbSNP Correlated rsID Position SNP Location D′ r² LOD rs3803662 51143842 rs4784227 51156689 0.968 0.881 31.08 rs3803662 51143842 rs3112572 51157948 1.000 0.055  1.64 rs3803662 51143842 rs3104747 51159425 1.000 0.055  1.64 rs3803662 51143842 rs3104748 51159860 1.000 0.055  1.64 rs3803662 51143842 rs3104750 51159990 1.000 0.055  1.64 rs3803662 51143842 rs3104758 51166534 1.000 0.055  1.64 rs3803662 51143842 rs3104759 51167030 1.000 0.055  1.64 rs3803662 51143842 rs9708611 51170166 1.000 0.169  4.56 rs3803662 51143842 rs12935019 51170538 1.000 0.088  4.04 rs3803662 51143842 rs4784230 51175614 1.000 0.085  4.19 rs3803662 51143842 rs11645620 51176454 1.000 0.085  4.19 rs3803662 51143842 rs3112633 51178078 1.000 0.085  4.19 rs3803662 51143842 rs3104766 51182036 0.766 0.239  7.55 rs3803662 51143842 rs3104767 51182239 0.626 0.167  4.88 rs3803662 51143842 rs3112625 51183053 0.671 0.188  5.62 rs3803662 51143842 rs12920540 51183114 0.676 0.195  5.84 rs3803662 51143842 rs3104774 51187203 0.671 0.188  5.62 rs3803662 51143842 rs7203671 51187646 0.671 0.188  5.62 rs3803662 51143842 rs3112617 51189218 0.666 0.177  5.44 rs3803662 51143842 rs11075551 51189465 0.666 0.177  5.44 rs3803662 51143842 rs12929797 51190445 0.676 0.19  5.87 rs3803662 51143842 rs3104780 51191415 0.671 0.184  5.65 rs3803662 51143842 rs12922061 51192501 0.832 0.631 19.14 rs3803662 51143842 rs3112612 51192665 0.671 0.184  5.65 rs3803662 51143842 rs3104784 51193866 0.666 0.177  5.44 rs3803662 51143842 rs12597685 51195281 0.671 0.184  5.65 rs3803662 51143842 rs3104788 51196004 0.666 0.177  5.44 rs3803662 51143842 rs3104800 51203877 0.625 0.17  4.99 rs3803662 51143842 rs3112609 51206232 0.599 0.163  4.86 rs3803662 51143842 rs3112600 51214089 0.311 0.016  0.57 rs3803662 51143842 rs3104807 51215026 0.302 0.014  0.52 rs3803662 51143842 rs3112594 51229030 0.522 0.065  1.56 rs3803662 51143842 rs4288991 51230665 0.238 0.052  1.53 rs3803662 51143842 rs3104820 51233304 0.528 0.069  1.60 rs3803662 51143842 rs3104824 51236594 0.362 0.067  1.93 rs3803662 51143842 rs3104826 51237406 0.362 0.067  1.93 rs3803662 51143842 rs3112588 51238502 0.354 0.062  1.80

TABLE 3 Surrogate markers for SNP rs4415084. Markers with a r2 greater than 0.05 to rs4415084 in the HAPMAP dataset (http://hapmap.ncbi.nlm.nih.gov) in a 1 Mbp interval flanking the marker was selected. Shown is the name of the correlated SNP, values for r2 and D′ to rs4415084 and the corresponding LOD value, as well as the position of the surrogate marker in NCB Build 36. DbSNP Correlated rsID Position SNP Location D′ r² LOD rs4415084 44698272 rs12522626 44721455 1.000 1.0 47.37 rs4415084 44698272 rs4571480 44722945 1.000 0.976 40.54 rs4415084 44698272 rs6451770 44727152 1.000 0.978 44.88 rs4415084 44698272 rs920328 44734808 1.000 0.893 39.00 rs4415084 44698272 rs920329 44738264 1.000 1.0 47.37 rs4415084 44698272 rs2218081 44740897 1.000 1.0 47.37 rs4415084 44698272 rs16901937 44744898 1.000 0.978 45.06 rs4415084 44698272 rs11747159 44773467 0.948 0.747 28.79 rs4415084 44698272 rs2330572 44776746 0.952 0.845 34.31 rs4415084 44698272 rs994793 44779004 0.952 0.848 34.49 rs4415084 44698272 rs1438827 44787713 0.948 0.749 29.76 rs4415084 44698272 rs7712949 44806102 0.948 0.746 29.19 rs4415084 44698272 rs11746980 44813635 0.952 0.848 34.49 rs4415084 44698272 rs16901964 44819012 0.949 0.768 30.54 rs4415084 44698272 rs727305 44831799 0.972 0.746 27.65 rs4415084 44698272 rs10462081 44836422 0.948 0.749 29.76 rs4415084 44698272 rs13183209 44839506 0.925 0.746 28.55 rs4415084 44698272 rs13159598 44841683 0.952 0.848 34.19 rs4415084 44698272 rs3761650 44844113 0.947 0.744 28.68 rs4415084 44698272 rs13174122 44846497 0.971 0.735 26.70 rs4415084 44698272 rs11746506 44848323 0.973 0.764 29.24 rs4415084 44698272 rs7720787 44853066 0.952 0.845 34.31 rs4415084 44698272 rs9637783 44855403 0.948 0.748 29.16 rs4415084 44698272 rs4457089 44857493 0.948 0.762 29.70 rs4415084 44698272 rs6896350 44868328 0.948 0.764 29.46 rs4415084 44698272 rs1371025 44869990 0.973 0.785 30.69 rs4415084 44698272 rs4596389 44872313 0.948 0.749 29.76 rs4415084 44698272 rs6451775 44872545 0.948 0.746 29.19 rs4415084 44698272 rs729599 44878017 0.948 0.748 29.16 rs4415084 44698272 rs987394 44882135 0.948 0.749 29.76 rs4415084 44698272 rs4440370 44889109 0.948 0.748 29.16 rs4415084 44698272 rs7703497 44892785 0.948 0.749 29.76 rs4415084 44698272 rs13362132 44894017 0.952 0.827 34.09 rs4415084 44698272 rs1438821 44894208 0.951 0.844 34.52

TABLE 4 Surrogate markers for SNP rs13387042. Markers with a r2 greater than 0.05 to rs13387042 in the HAPMAP dataset (http://hapmap.ncbi.nlm.nih.gov) in a 1 Mbp interval flanking the marker was selected. Shown is the name of the correlated SNP, values for r2 and D′ to rs13387042 and the corresponding LOD value, as well as the position of the surrogate marker in NCB Build 36. DbSNP rsID Position Correlated SNP Location D′ r² LOD rs13387042 217614077 rs4621152 217617230 0.865 0.364 15.30 rs13387042 217614077 rs6721996 217617708 1.000 0.979 50.46 rs13387042 217614077 rs12694403 217623659 0.955 0.33 14.24 rs13387042 217614077 rs17778427 217631258 1.000 0.351 16.12 rs13387042 217614077 rs17835044 217631850 1.000 0.351 16.12 rs13387042 217614077 rs7588345 217632061 1.000 0.193  8.93 rs13387042 217614077 rs7562029 217632506 1.000 0.413 20.33 rs13387042 217614077 rs13000023 217632639 0.949 0.287 12.20 rs13387042 217614077 rs13409592 217634573 0.933 0.192  7.69 rs13387042 217614077 rs2372957 217635302 0.855 0.168  5.97 rs13387042 217614077 rs16856888 217638914 0.363 0.101  3.31 rs13387042 217614077 rs16856890 217639976 0.371 0.101  3.29 rs13387042 217614077 rs7598926 217640464 0.382 0.109  3.60 rs13387042 217614077 rs6734010 217643676 0.543 0.217  7.90 rs13387042 217614077 rs13022815 217644369 0.800 0.319 12.94 rs13387042 217614077 rs16856893 217645298 0.739 0.109  3.45 rs13387042 217614077 rs13011060 217646422 0.956 0.352 14.71 rs13387042 217614077 rs4674132 217646764 0.802 0.327 13.10 rs13387042 217614077 rs16825211 217647249 0.912 0.326 12.95 rs13387042 217614077 rs41521045 217647581 0.903 0.112  4.70 rs13387042 217614077 rs2372960 217650960 0.678 0.058  2.12 rs13387042 217614077 rs2372967 217676158 0.326 0.052  1.97 rs13387042 217614077 rs3843337 217677680 0.326 0.052  1.97 rs13387042 217614077 rs2372972 217679386 0.375 0.062  2.28 rs13387042 217614077 rs9677455 217680497 0.375 0.062  2.28 rs13387042 217614077 rs12464728 217686802 0.478 0.073  2.54

In one embodiment, the methods of the present disclosure encompass assessing all of the SNPs shown in Table 6 or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

Table 6 and Table 7 recite overlapping SNPs. It will be appreciated that when selecting SNPs for assessment the same SNP will not be selected twice. For convenience, the SNPs in Table 6 have been separated into Tables 7 and 8. Table 7 lists SNPs common across Caucasians, African American and Hispanic populations. Table 8 lists SNPs that are not common across Caucasians, African American and Hispanic populations.

In a further embodiment, between 72 and 88, between 73 and 87, between 74 and 86, between 75 and 85, between 76 and 87, between 75 and 86, between 76 and 85, between 77 and 84, between 78 and 83, between 79 and 82, between 80 and 81 single nucleotide polymorphisms are assessed, wherein at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, of the SNPs shown in Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof are assessed, with any remaining SNPs being selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, the number of SNPs assessed is based on the net reclassification improvement in risk prediction calculated using net reclassification index (NRI) (Pencina et al., 2008).

In an embodiment, the net reclassification improvement of the methods of the present disclosure is greater than 0.01.

In a further embodiment, the net reclassification improvement of the methods of the present disclosure is greater than 0.05.

In yet another embodiment, the net reclassification improvement of the methods of the present disclosure is greater than 0.1.

Calculating Composite SNP Relative Risk “SNP Risk”

An individual's composite SNP relative risk score (“SNP risk”) can be defined as the product of genotype relative risk values for each SNPs assessed. A log-additive risk model can then be used to define three genotypes AA, AB, and BB for a single SNP having relative risk values of 1, OR, and OR², under a rare disease model, where OR is the previously reported disease odds ratio for the high-risk allele, B, vs the low-risk allele, A. If the B allele has frequency (p), then these genotypes have population frequencies of (1−p)², 2p(1−p), and p², assuming Hardy-Weinberg equilibrium. The genotype relative risk values for each SNP can then be scaled so that based on these frequencies the average relative risk in the population is 1. Specifically, given the unsealed population average relative risk:

(μ)=(1−p)²+2p(1−p)OR+p ²OR²

Adjusted risk values 1/μ, OR/μ, and OR²/μ are used for AA, AB, and BB genotypes. Missing genotypes are assigned a relative risk of 1.

Similar calculations can be performed for non-SNP polymorphisms.

Combined Clinical Assessment×Genetic Risk Score

In combining the clinical risk assessment with the genetic risk assessment to obtain the “risk” of a human female subject for developing breast cancer, the following formula can be used:

[Risk (i.e. Clinical Evaluation×SNP risk)]=[Clinical Evaluation risk]×SNP₁×SNP₂×SNP₃×SNP₄×SNP₅×SNP₆×SNP₇,×SNP₈, . . . ×SNP₇₂ etc.

This example relates to when the polymorphisms are SNPs but similar procedures can be used for non-SNP polymorphisms.

Where Clinical Evaluation is the risk score provided by the clinical evaluation, and SNP₁ to SNP₇₂ are relative risk scores for the individual SNPs, each scaled to have a population average of 1 as outlined above. Because the SNP risk scores have been “centred” to have a population average risk of 1, if one assumes independence among the SNPs, then the population average risk across all genotypes for the combined score is consistent with the underlying Clinical Evaluation risk estimate.

In an embodiment the risk of a human female subject for developing breast cancer is calculated by [Clinical Evaluation 5-year risk]×SNP₁×SNP₂×SNP₃×SNP₄×SNP₅×SNP₆×SNP₇,×SNP₈, . . . ×SNP₇₂ etc.

In another embodiment the risk of a human female subject for developing breast cancer is calculated by [Clinical Evaluation risk]×SNP₁×SNP₂×SNP₃×SNP₄×SNP₅×SNP₆×SNP₇,×SNP₈,×SNP₇₂ etc.

In another embodiment the risk of a human female subject for developing breast cancer is calculated by [Clinical Evaluation lifetime risk]×SNP₁×SNP₂×SNP₃×SNP₄×SNP₅×SNP₆×SNP₇,×SNP₈, . . . ×SNP₇₂ etc.

In an embodiment, the Clinical Evaluation is performed using the Gail model to provide a Gail Risk Score. In this embodiment, the risk (i.e. combined 5-year Gail×SNP risk) score is provided by:

[Risk (i.e. Gail 5-year risk×SNP risk)]=[Gail 5-year risk]×SNP₁×SNP₂×SNP₃×SNP₄×SNP₅×SNP₆×SNP₇,×SNP₈, . . . ×SNP₇₂ etc.

In an embodiment, the risk [Gail 5-year risk×SNP risk] is used to determine whether estrogen receptor therapy should be offered to a subject to reduce the subjects risk. In this embodiment, the threshold level of risk is preferably (GAIL index >1.66% for 5-year risk).

In another embodiment, the risk score is determined by combined Gail lifetime risk×SNP risk provided by:

[Risk (i.e. Gail lifetime risk×SNP risk)]=[Gail lifetime risk]×SNP₁×SNP₂×SNP₃×SNP₄×SNP₅×SNP₆×SNP₇,×SNP₈, . . . ×SNP₇₂ etc.

In a further embodiment, the risk [Gail lifetime risk×SNP risk] is used to determine whether a subject should be enrolled screening breast MRIC and mammography program. In this embodiment, the threshold level is preferably greater than about (20% lifetime risk).

In an embodiment the methods of the present disclosure comprise combining the clinical risk assessment with the genetic risk assessment to obtain the risk of a human female subject for developing breast cancer.

It is envisaged that the “risk” of a human female subject for developing breast cancer can be provided as a relative risk (or risk ratio) or an absolute risk as required.

In an embodiment, the clinical risk assessment is combined with the genetic risk assessment to obtain the “relative risk” of a human female subject for developing breast cancer. Relative risk (or risk ratio), measured as the incidence of a disease in individuals with a particular characteristic (or exposure) divided by the incidence of the disease in individuals without the characteristic, indicates whether that particular exposure increases or decreases risk. Relative risk is helpful to identify characteristics that are associated with a disease, but by itself is not particularly helpful in guiding screening decisions because the frequency of the risk (incidence) is cancelled out.

In another embodiment, the clinical risk assessment is combined with the genetic risk assessment to obtain the “absolute risk” of a human female subject for developing breast cancer. Absolute risk is the numerical probability of a human female subject developing breast cancer within a specified period (e.g. 5, 10, 15, 20 or more years). It reflects a human female subjects risk of developing breast cancer in so far as it does not consider various risk factors in isolation.

Treatment

After performing the methods of the present disclosure treatment may be prescribed or administered to the subject.

One of skill in the art will appreciate that breast cancer is a heterogeneous disease with distinct clinical outcomes (Sorlie et al., 2001). For example, it is discussed in the art that breast cancer may be estrogen receptor positive or estrogen receptor negative.

In one embodiment, it is not envisaged that the methods of the present disclosure be limited to assessing the risk of developing a particular type or subtype of breast cancer. For example, it is envisaged that the methods of the present disclosure can be used to assess the risk of developing estrogen receptor positive or estrogen receptor negative breast cancer.

In another embodiment, the methods of the present disclosure are used to assess the risk of developing estrogen receptor positive breast cancer.

In another embodiment, the methods of the present disclosure are used to assess the risk of developing estrogen receptor negative breast cancer.

In another embodiment, the methods of the present disclosure are used to assess the risk of developing metastatic breast cancer.

In an example, a therapy that inhibits oestrogen is prescribed or administered to the subject.

In another example, a chemopreventative is prescribed or administered to the subject.

There are two main classes of drugs currently utilized for breast cancer chemoprevention:

-   -   (1) Selective Estrogen Receptor Modulators (SERMs) which block         estrogen molecules from binding to their associated cellular         receptor. This class of drugs includes for example Tamoxifen and         Raloxifene.     -   (2) Aromatase Inhibitors which inhibit the conversion of         androgens into estrogens by the aromatase enzyme Ie reducing the         production of estrogens. This class of drugs includes for         example Exemestane, Letrozole, Anastrozole, Vorozole,         Formestane, Fadrozole.

In an example, a SERM or an aromatase inhibitor is prescribed or administered to the subject.

In an example, Tamoxifen, Raloxifene, Exemestane, Letrozole, Anastrozole, Vorozole, Formestane or Fadrozole is prescribed or administered to a subject.

In an embodiment, the methods of the present disclosure are used to assess the risk of a human female subject for developing breast cancer and administering a treatment appropriate for the risk of developing breast cancer. For example, when performing the methods of the present disclosure indicates a high risk of breast cancer an aggressive chemopreventative treatment regimen can be established. In contrast, when performing the methods of the present disclosure indicates a moderate risk of breast cancer a less aggressive chemopreventative treatment regimen can be established. Alternatively, when performing the methods of the present disclosure indicates a low risk of breast cancer a chemopreventative treatment regimen need not be established. It is envisaged that the methods of the present disclosure can be performed over time so that the treatment regimen can be modified in accordance with the subjects risk of developing breast cancer.

Marker Detection Strategies

Amplification primers for amplifying markers (e.g., marker loci) and suitable probes to detect such markers or to genotype a sample with respect to multiple marker alleles, can be used in the disclosure. For example, primer selection for long-range PCR is described in U.S. Ser. No. 10/042,406 and U.S. Ser. No. 10/236,480; for short-range PCR, U.S. Ser. No. 10/341,832 provides guidance with respect to primer selection. Also, there are publicly available programs such as “Oligo” available for primer design. With such available primer selection and design software, the publicly available human genome sequence and the polymorphism locations, one of skill can construct primers to amplify the SNPs to practice the disclosure. Further, it will be appreciated that the precise probe to be used for detection of a nucleic acid comprising a SNP (e.g., an amplicon comprising the SNP) can vary, e.g., any probe that can identify the region of a marker amplicon to be detected can be used in conjunction with the present disclosure. Further, the configuration of the detection probes can, of course, vary. Thus, the disclosure is not limited to the sequences recited herein.

Indeed, it will be appreciated that amplification is not a requirement for marker detection, for example one can directly detect unamplified genomic DNA simply by performing a Southern blot on a sample of genomic DNA.

Typically, molecular markers are detected by any established method available in the art, including, without limitation, allele specific hybridization (ASH), detection of single nucleotide extension, array hybridization (optionally including ASH), or other methods for detecting single nucleotide polymorphisms (SNPs), amplified fragment length polymorphism (AFLP) detection, amplified variable sequence detection, randomly amplified polymorphic DNA (RAPD) detection, restriction fragment length polymorphism (RFLP) detection, self-sustained sequence replication detection, simple sequence repeat (SSR) detection, and single-strand conformation polymorphisms (SSCP) detection.

Examples of oligonucleotide primers useful for amplifying nucleic acids comprising SNPs associated with breast cancer are provided in Table 5. As the skilled person will appreciate, the sequence of the genomic region to which these oligonucleotides hybridize can be used to design primers which are longer at the 5′ and/or 3′ end, possibly shorter at the 5′ and/or 3′ (as long as the truncated version can still be used for amplification), which have one or a few nucleotide differences (but nonetheless can still be used for amplification), or which share no sequence similarity with those provided but which are designed based on genomic sequences close to where the specifically provided oligonucleotides hybridize and which can still be used for amplification.

TABLE 5  Examples of oligonucleotide primers useful for the disclosure. Name Sequence rs889312_for TATGGGAAGGAGTCGTTGAG  (SEQ ID NO: 1) rs6504950_for CTGAATCACTCCTTGCCAAC  (SEQ ID NO: 2) rs4973768_for CAAAATGATCTGACTACTCC  (SEQ ID NO: 3) rs4415084_for TGACCAGTGCTGTATGTATC  (SEQ ID NO: 4) rs3817198_for TCTCACCTGATACCAGATTC  (SEQ ID NO: 5) rs3803662_for TCTCTCCTTAATGCCTCTAT  (SEQ ID NO: 6) rs2981582_for ACTGCTGCGGGTTCCTAAAG  (SEQ ID NO: 7) rs13387042_for GGAAGATTCGATTCAACAAGG  (SEQ ID NO: 8) rs13281615_for GGTAACTATGAATCTCATC  (SEQ ID NO: 9) rs11249433_for AAAAAGCAGAGAAAGCAGGG  (SEQ ID NO: 10) rs889312_rev AGATGATCTCTGAGATGCCC  (SEQ ID NO: 11) rs6504950_rev CCAGGGTTTGTCTACCAAAG  (SEQ ID NO: 12) rs4973768_rev AATCACTTAAAACAAGCAG  (SEQ ID NO: 13) rs4415084_rev CACATACCTCTACCTCTAGC  (SEQ ID NO: 14) rs3817198_rev TTCCCTAGTGGAGCAGTGG  (SEQ ID NO: 15) rs3803662_rev CTTTCTTCGCAAATGGGTGG  (SEQ ID NO: 16) rs2981582_rev GCACTCATCGCCACTTAATG  (SEQ ID NO: 17) rs13387042_rev GAACAGCTAAACCAGAACAG  (SEQ ID NO: 18) rs13281615_rev ATCACTCTTATTTCTCCCCC  (SEQ ID NO: 19) rs11249433_rev TGAGTCACTGTGCTAAGGAG  (SEQ ID NO: 20)

In some embodiments, the primers of the disclosure are radiolabelled, or labelled by any suitable means (e.g., using a non-radioactive fluorescent tag), to allow for rapid visualization of differently sized amplicons following an amplification reaction without any additional labelling step or visualization step. In some embodiments, the primers are not labelled, and the amplicons are visualized following their size resolution, e.g., following agarose or acrylamide gel electrophoresis. In some embodiments, ethidium bromide staining of the PCR amplicons following size resolution allows visualization of the different size amplicons.

It is not intended that the primers of the disclosure be limited to generating an amplicon of any particular size. For example, the primers used to amplify the marker loci and alleles herein are not limited to amplifying the entire region of the relevant locus, or any subregion thereof. The primers can generate an amplicon of any suitable length for detection. In some embodiments, marker amplification produces an amplicon at least 20 nucleotides in length, or alternatively, at least 50 nucleotides in length, or alternatively, at least 100 nucleotides in length, or alternatively, at least 200 nucleotides in length. Amplicons of any size can be detected using the various technologies described herein. Differences in base composition or size can be detected by conventional methods such as electrophoresis.

Some techniques for detecting genetic markers utilize hybridization of a probe nucleic acid to nucleic acids corresponding to the genetic marker (e.g., amplified nucleic acids produced using genomic DNA as a template). Hybridization formats, including, but not limited to: solution phase, solid phase, mixed phase, or in situ hybridization assays are useful for allele detection. An extensive guide to the hybridization of nucleic acids is found in Tijssen (1993) Laboratory Techniques in Biochemistry and Molecular Biology—Hybridization with Nucleic Acid Probes Elsevier, New York, as well as in Sambrook et al. (supra).

PCR detection using dual-labelled fluorogenic oligonucleotide probes, commonly referred to as “TaqMan™” probes, can also be performed according to the present disclosure. These probes are composed of short (e.g., 20-25 base) oligodeoxynucleotides that are labelled with two different fluorescent dyes. On the 5′ terminus of each probe is a reporter dye, and on the 3′ terminus of each probe a quenching dye is found. The oligonucleotide probe sequence is complementary to an internal target sequence present in a PCR amplicon. When the probe is intact, energy transfer occurs between the two fluorophores and emission from the reporter is quenched by the quencher by FRET. During the extension phase of PCR, the probe is cleaved by 5′ nuclease activity of the polymerase used in the reaction, thereby releasing the reporter from the oligonucleotide-quencher and producing an increase in reporter emission intensity. Accordingly, TaqMan™ probes are oligonucleotides that have a label and a quencher, where the label is released during amplification by the exonuclease action of the polymerase used in amplification. This provides a real time measure of amplification during synthesis. A variety of TaqMan™ reagents are commercially available, e.g., from Applied Biosystems (Division Headquarters in Foster City, Calif.) as well as from a variety of specialty vendors such as Biosearch Technologies (e.g., black hole quencher probes). Further details regarding dual-label probe strategies can be found, e.g., in WO 92/02638.

Other similar methods include e.g. fluorescence resonance energy transfer between two adjacently hybridized probes, e.g., using the “LightCycler®” format described in U.S. Pat. No. 6,174,670.

Array-based detection can be performed using commercially available arrays, e.g., from Affymetrix (Santa Clara, Calif.) or other manufacturers. Reviews regarding the operation of nucleic acid arrays include Sapolsky et al. (1999); Lockhart (1998); Fodor (1997a); Fodor (1997b) and Chee et al. (1996). Array based detection is one preferred method for identification markers of the disclosure in samples, due to the inherently high-throughput nature of array based detection.

The nucleic acid sample to be analyzed is isolated, amplified and, typically, labelled with biotin and/or a fluorescent reporter group. The labelled nucleic acid sample is then incubated with the array using a fluidics station and hybridization oven. The array can be washed and or stained or counter-stained, as appropriate to the detection method. After hybridization, washing and staining, the array is inserted into a scanner, where patterns of hybridization are detected. The hybridization data are collected as light emitted from the fluorescent reporter groups already incorporated into the labelled nucleic acid, which is now bound to the probe array. Probes that most clearly match the labelled nucleic acid produce stronger signals than those that have mismatches. Since the sequence and position of each probe on the array are known, by complementarity, the identity of the nucleic acid sample applied to the probe array can be identified.

Correlating Markers to Phenotypes

These correlations can be performed by any method that can identify a relationship between an allele and a phenotype, or a combination of alleles and a combination of phenotypes. For example, alleles in genes or loci defined herein can be correlated with one or more breast cancer phenotypes. Most typically, these methods involve referencing a look up table that comprises correlations between alleles of the polymorphism and the phenotype. The table can include data for multiple allele-phenotype relationships and can take account of additive or other higher order effects of multiple allele-phenotype relationships, e.g., through the use of statistical tools such as principle component analysis, heuristic algorithms, etc.

Correlation of a marker to a phenotype optionally includes performing one or more statistical tests for correlation. Many statistical tests are known, and most are computer-implemented for ease of analysis. A variety of statistical methods of determining associations/correlations between phenotypic traits and biological markers are known and can be applied to the present disclosure. Hartl (1981) A Primer of Population Genetics Washington University, Saint Louis Sinauer Associates, Inc. Sunderland, Mass. ISBN: 0-087893-271-2. A variety of appropriate statistical models are described in Lynch and Walsh (1998) Genetics and Analysis of Quantitative Traits, Sinauer Associates, Inc. Sunderland Mass. ISBN 0-87893-481-2. These models can, for example, provide for correlations between genotypic and phenotypic values, characterize the influence of a locus on a phenotype, sort out the relationship between environment and genotype, determine dominance or penetrance of genes, determine maternal and other epigenetic effects, determine principle components in an analysis (via principle component analysis, or “PCA”), and the like. The references cited in these texts provides considerable further detail on statistical models for correlating markers and phenotype.

In addition to standard statistical methods for determining correlation, other methods that determine correlations by pattern recognition and training, such as the use of genetic algorithms, can be used to determine correlations between markers and phenotypes. This is particularly useful when identifying higher order correlations between multiple alleles and multiple phenotypes. To illustrate, neural network approaches can be coupled to genetic algorithm-type programming for heuristic development of a structure-function data space model that determines correlations between genetic information and phenotypic outcomes.

In any case, essentially any statistical test can be applied in a computer implemented model, by standard programming methods, or using any of a variety of “off the shelf” software packages that perform such statistical analyses, including, for example, those noted above and those that are commercially available, e.g., from Partek Incorporated (St. Peters, Mo.; www.partek.com), e.g., that provide software for pattern recognition (e.g., which provide Partek Pro 2000 Pattern Recognition Software).

Additional details regarding association studies can be found in U.S. Ser. No. 10/106,097, U.S. Ser. No. 10/042,819, U.S. Ser. No. 10/286,417, U.S. Ser. No. 10/768,788, U.S. Ser. No. 10/447,685, U.S. Ser. No. 10/970,761, and U.S. Pat. No. 7,127,355.

Systems for performing the above correlations are also a feature of the disclosure. Typically, the system will include system instructions that correlate the presence or absence of an allele (whether detected directly or, e.g., through expression levels) with a predicted phenotype.

Optionally, the system instructions can also include software that accepts diagnostic information associated with any detected allele information, e.g., a diagnosis that a subject with the relevant allele has a particular phenotype. This software can be heuristic in nature, using such inputted associations to improve the accuracy of the look up tables and/or interpretation of the look up tables by the system. A variety of such approaches, including neural networks, Markov modelling, and other statistical analysis are described above.

Polymorphic Profiling

The disclosure provides methods of determining the polymorphic profile of an individual at the SNPs outlined in the present disclosure (Table 6) or SNPs in linkage disequilibrium with one or more thereof.

The polymorphic profile constitutes the polymorphic forms occupying the various polymorphic sites in an individual. In a diploid genome, two polymorphic forms, the same or different from each other, usually occupy each polymorphic site. Thus, the polymorphic profile at sites X and Y can be represented in the form X (x1, x1), and Y (y1, y2), wherein x1, x1 represents two copies of allele x1 occupying site X and y1, y2 represent heterozygous alleles occupying site Y.

The polymorphic profile of an individual can be scored by comparison with the polymorphic forms associated with resistance or susceptibility to breast cancer occurring at each site. The comparison can be performed on at least, e.g., 1, 2, 5, 10, 25, 50, or all of the polymorphic sites, and optionally, others in linkage disequilibrium with them. The polymorphic sites can be analyzed in combination with other polymorphic sites.

Polymorphic profiling is useful, for example, in selecting agents to affect treatment or prophylaxis of breast cancer in a given individual. Individuals having similar polymorphic profiles are likely to respond to agents in a similar way.

Polymorphic profiling is also useful for stratifying individuals in clinical trials of agents being tested for capacity to treat breast cancer or related conditions. Such trials are performed on treated or control populations having similar or identical polymorphic profiles (see EP 99965095.5), for example, a polymorphic profile indicating an individual has an increased risk of developing breast cancer. Use of genetically matched populations eliminates or reduces variation in treatment outcome due to genetic factors, leading to a more accurate assessment of the efficacy of a potential drug.

Polymorphic profiling is also useful for excluding individuals with no predisposition to breast cancer from clinical trials. Including such individuals in the trial increases the size of the population needed to achieve a statistically significant result. Individuals with no predisposition to breast cancer can be identified by determining the numbers of resistances and susceptibility alleles in a polymorphic profile as described above. For example, if a subject is genotyped at ten sites in ten genes of the disclosure associated with breast cancer, twenty alleles are determined in total. If over 50% and alternatively over 60% or 75% percent of these are resistance genes, the individual is unlikely to develop breast cancer and can be excluded from the trial.

In other embodiments, stratifying individuals in clinical trials may be accomplished using polymorphic profiling in combination with other stratification methods, including, but not limited to, family history, risk models (e.g., Gail Score, Claus model), clinical phenotypes (e.g., atypical lesions, breast density), and specific candidate biomarkers.

Computer Implemented Method

It is envisaged that the methods of the present disclosure may be implemented by a system such as a computer implemented method. For example, the system may be a computer system comprising one or a plurality of processors which may operate together (referred to for convenience as “processor”) connected to a memory. The memory may be a non-transitory computer readable medium, such as a hard drive, a solid state disk or CD-ROM. Software, that is executable instructions or program code, such as program code grouped into code modules, may be stored on the memory, and may, when executed by the processor, cause the computer system to perform functions such as determining that a task is to be performed to assist a user to determine the risk of a human female subject for developing breast cancer; receiving data indicating the clinical risk and genetic risk of the female subject developing breast cancer, wherein the genetic risk was derived by detecting, in a biological sample derived from the female subject, at least 72 single nucleotide polymorphisms associated with breast cancer, wherein at least 67 of the single nucleotide polymorphisms are selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof and the remaining single nucleotide polymorphisms are selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof; processing the data to combine the clinical risk with the genetic risk assessment to obtain the risk of a human female subject for developing breast cancer; outputting the risk of a human female subject for developing breast cancer.

For example, the memory may comprise program code which when executed by the processor causes the system to determine at least 72 single nucleotide polymorphisms associated with breast cancer, wherein at least 67 of the single nucleotide polymorphisms are selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof and the remaining single nucleotide polymorphisms are selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof, or receive data indicating at least 72 single nucleotide polymorphisms associated with breast cancer, wherein at least 67 of the single nucleotide polymorphisms are selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof and the remaining single nucleotide polymorphisms are selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof; process the data to combine the clinical risk with the genetic risk assessment to obtain the risk of a human female subject for developing breast cancer; report the risk of a human female subject for developing breast cancer.

In another embodiment, the system may be coupled to a user interface to enable the system to receive information from a user and/or to output or display information. For example, the user interface may comprise a graphical user interface, a voice user interface or a touchscreen.

In an embodiment, the program code may causes the system to determine the “SNP risk”.

In an embodiment, the program code may causes the system to determine Combined Clinical assessment×Genetic Risk (for example SNP risk).

In an embodiment, the system may be configured to communicate with at least one remote device or server across a communications network such as a wireless communications network. For example, the system may be configured to receive information from the device or server across the communications network and to transmit information to the same or a different device or server across the communications network. In other embodiments, the system may be isolated from direct user interaction.

In another embodiment, performing the methods of the present disclosure to assess the risk of a human female subject for developing breast cancer, enables establishment of a diagnostic or prognostic rule based on the the clinical risk and genetic risk of the female subject developing breast cancer. For example, the diagnostic or prognostic rule can be based on the Combined Clinical assessment×SNP Risk Score relative to a control, standard or threshold level of risk.

In an embodiment, the threshold level of risk is the level recommended by the American Cancer Society (ACS) guidelines for screening breast MRIC and mammography. In this example, the threshold level is preferably greater than about (20% lifetime risk).

In another embodiment, the threshold level of risk is the level recommended American Society of Clinical Oncology (ASCO) for offering an estrogen receptor therapy to reduce a subjects risk. In this embodiment, the threshold level of risk is preferably (GAIL index >1.66% for 5-year risk).

In another embodiment, the diagnostic or prognostic rule is based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between a population of SNPs and disease status observed in training data (with known disease status) to infer relationships which are then used to determine the risk of a human female subject for developing breast cancer in subjects with an unknown risk. An algorithm is employed which provides an risk of a human female subject developing breast cancer. The algorithm performs a multivariate or univariate analysis function.

Kits and Products

In an embodiment, the present invention provides a kit comprising at least 72 sets of primers for amplifying 72 or more nucleic acids, wherein the 72 or more nucleic acids comprise a single nucleotide polymorphism selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, at least 67, at least 68, at least 69, at least 70 sets of the primers amplify nucleic acids comprising a single nucleotide polymorphism selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium thereof.

Examples of primers suitable for a kit of the invention are provided in Table 5.

However, as would be appreciated by those of skill in the art, once a SNP is identified, primers can be designed to amplify the SNP as a matter of routine. Various software programs are freely available that can suggest suitable primers for amplifying SNPs of interest.

Again, it would be known to those of skill in the art that PCR primers of a PCR primer pair can be designed to specifically amplify a region of interest from human DNA. In the context of the present disclosure, the region of interest contains the single-base variation (e.g. single-nucleotide polymorphism, SNP) which shall be genotyped. Each PCR primer of a PCR primer pair can be placed adjacent to a particular single-base variation on opposing sites of the DNA sequence variation. Furthermore, PCR primers can been designed to avoid any known DNA sequence variation and repetitive DNA sequences in their PCR primer binding sites.

The kit may further comprise other reagents required to perform an amplification reaction such as a buffer, nucleotides and/or a polymerase, as well as reagents for extracting nucleic acids from a sample.

Array based detection is one preferred method for assessing the SNPs of the disclosure in samples, due to the inherently high-throughput nature of array based detection.

A variety of probe arrays have been described in the literature and can be used in the context of the present disclosure for detection of SNPs that can be correlated to breast cancer. For example, DNA probe array chips are used in one embodiment of the disclosure. The recognition of sample DNA by the set of DNA probes takes place through DNA hybridization. When a DNA sample hybridizes with an array of DNA probes, the sample binds to those probes that are complementary to the sample DNA sequence. By evaluating to which probes the sample DNA for an individual hybridizes more strongly, it is possible to determine whether a known sequence of nucleic acid is present or not in the sample, thereby determining whether a marker found in the nucleic acid is present.

In an embodiment, the present invention provides a genetic array comprising at least 72 sets of probes for hybridising to 72 or more nucleic acids, wherein the 72 or more nucleic acids comprise a single nucleotide polymorphism selected from Table 6, or a single nucleotide polymorphism in linkage disequilibrium with one or more thereof.

In an embodiment, at least 67, at least 68, at least 69, at least 70 sets of the probes hybridise to nucleic acids comprising a single nucleotide polymorphism selected from Table 7, or a single nucleotide polymorphism in linkage disequilibrium thereof.

EXAMPLES Example 1 SNPs Indicative of Breast Cancer Risk

SNPs indicative of breast cancer risk are shown in Table 6. 88 SNPs have been identified in total. 77 SNPs are informative in Caucasians, 78 SNPs are informative in African Americans and 82 are informative in Hispanics. 70 SNPs are informative in Caucasians, African Americans and Hispanics (indicated by horizontal stripe pattern; see also Table 7). The remaining 18 SNPs (see Table 8) are informative in either Caucasians (indicated by dark trellis pattern; see also Table 9), African Americans (indicated by downward diagonal stripe pattern; see also Table 10) and/or Hispanics (indicated by light grid pattern; see also Table 11).

TABLE 6 SNPs indicative of breast cancer risk (n = 88)

TABLE 7 SNPs common across Caucasians, African American and Hispanic populations (n = 70)

TABLE 8 SNPs not common across Caucasians, African American and Hispanic populations (n = 18)

TABLE 9 Caucasian SNPs (n = 77). Alleles represented as major/minor (eg for rs616488 A is the common allele and G less common). OR minor allele numbers below 1 means the minor allele is not the risk allele, whereas when above 1 the minor allele is the risk allele. Minor OR allele Minor SNP Chromosome Alleles frequency Allele μ Adjusted Risk Score rs616488  1 A/G 0.33 0.9417 0.96 AA 1.04 GA 0.98 GG 0.92 rs11552449  1 C/T 0.17 1.0810 1.03 CC 0.97 TC 1.05 TT 1.14 rs11249433  1 A/G 0.40 1.0993 1.08 AA 0.93 GA 1.02 GG 1.12 rs6678914  1 G/A 0.414 0.9890 0.99 GG 1.01 AG 1.00 AA 0.99 rs4245739  1 A/C 0.258 1.0291 1.02 AA 0.99 CA 1.01 CC 1.04 rs12710696  2 G/A 0.357 1.0387 1.03 GG 0.97 AG 1.01 AA 1.05 rs4849887  2 C/T 0.098 0.9187 0.98 CC 1.02 TC 0.93 TT 0.86 rs2016394  2 G/A 0.48 0.9504 0.95 GG 1.05 AG 1.00 AA 0.95 rs1550623  2 A/G 0.16 0.9445 0.98 AA 1.02 GA 0.96 GG 0.91 rs1045485  2 G/C 0.13 0.9644 0.99 GG 1.01 CG 0.97 CC 0.94 rs13387042  2 A/G 0.49 0.8794 0.89 AA 1.13 GA 0.99 GG 0.87 rs16857609  2 C/T 0.26 1.0721 1.04 CC 0.96 TC 1.03 TT 1.11 rs6762644  3 A/G 0.4 1.0661 1.05 AA 0.95 GA 1.01 GG 1.08 rs4973768  3 C/T 0.47 1.0938 1.09 CC 0.92 TC 1.00 TT 1.10 rs12493607  3 G/C 0.35 1.0529 1.04 GG 0.96 CG 1.01 CC 1.07 rs7696175  4 rs9790517  4 C/T 0.23 1.0481 1.02 CC 0.98 TC 1.03 TT 1.07 rs6828523  4 C/A 0.13 0.9056 0.98 CC 1.03 AC 0.93 AA 0.84 rs4415084  5 rs10069690  5 C/T 0.26 1.0242 1.01 CC 0.99 TC 1.01 TT 1.04 rs7726159  5 C/A 0.338 1.0359 1.02 CC 0.98 AC 1.01 AA 1.05 rs2736108  5 C/T 0.292 0.9379 0.96 CC 1.04 TC 0.97 TT 0.91 rs10941679  5 A/G 0.25 1.1198 1.06 AA 0.94 GA 1.06 GG 1.18 rs889312  5 A/C 0.28 1.1176 1.07 AA 0.94 CA 1.05 CC 1.17 rs10472076  5 T/C 0.38 1.0419 1.03 TT 0.97 CT 1.01 CC 1.05 rs2067980  5 rs1353747  5 T/G 0.095 0.9213 0.99 TT 1.02 GT 0.94 GG 0.86 rs1432679  5 A/G 0.43 1.0670 1.06 AA 0.94 GA 1.01 GG 1.08 rs11242675  6 T/C 0.39 0.9429 0.96 TT 1.05 CT 0.99 CC 0.93 rs204247  6 A/G 0.43 1.0503 1.04 AA 0.96 GA 1.01 GG 1.06 rs17529111  6 A/G 0.218 1.0457 1.02 AA 0.98 GA 1.03 GG 1.07 rs2180341  6 rs9485370  6 rs12662670  6 T/G 0.073 1.1392 1.02 TT 0.98 GT 1.12 GG 1.27 rs3757318  6 rs2046210  6 G/A 0.34 1.0471 1.03 GG 0.97 AG 1.01 AA 1.06 rs17157903  7 rs720475  7 G/A 0.25 0.9452 0.97 GG 1.03 AG 0.97 AA 0.92 rs9693444  8 C/A 0.32 1.0730 1.05 CC 0.95 AC 1.02 AA 1.10 rs6472903  8 T/G 0.18 0.9124 0.97 TT 1.03 GT 0.94 GG 0.86 rs2943559  8 A/G 0.07 1.1334 1.02 AA 0.98 GA 1.11 GG 1.26 rs13281615  8 A/G 0.41 1.0950 1.08 AA 0.93 GA 1.01 GG 1.11 rs11780156  8 C/T 0.16 1.0691 1.02 CC 0.98 TC 1.05 TT 1.12 rs1011970  9 G/T 0.17 1.0502 1.02 GG 0.98 TG 1.03 TT 1.08 rs10759243  9 C/A 0.39 1.0542 1.04 CC 0.96 AC 1.01 AA 1.07 rs865686  9 T/G 0.38 0.8985 0.92 TT 1.08 GT 0.97 GG 0.87 rs2380205 10 C/T 0.44 0.9771 0.98 CC 1.02 TC 1.00 TT 0.97 rs7072776 10 G/A 0.29 1.0581 1.03 GG 0.97 AG 1.02 AA 1.08 rs11814448 10 A/C 0.02 1.2180 1.01 AA 0.99 CA 1.21 CC 1.47 rs10822013 10 rs10995190 10 G/A 0.16 0.8563 0.95 GG 1.05 AG 0.90 AA 0.77 rs704010 10 C/T 0.38 1.06991 .05 CC 0.95 TC 1.02 TT 1.09 rs7904519 10 A/G 0.46 1.0584 1.05 AA 0.95 GA 1.00 GG 1.06 rs2981579 10 G/A 0.4 1.2524 1.21 GG 0.83 AG 1.03 AA 1.29 rs2981582 10 rs11199914 10 C/T 0.32 0.9400 0.96 CC 1.04 TC 0.98 TT 0.92 rs3817198 11 T/C 0.31 1.0744 1.05 TT 0.96 CT 1.03 CC 1.10 rs3903072 11 G/T 0.47 0.9442 0.95 GG 1.05 TG 1.00 TT 0.94 rs554219 11 C/G 0.112 1.1238 1.03 CC 0.97 GC 1.09 GG 1.23 rs614367 11 rs78540526 11 C/T 0.032 1.1761 1.01 CC 0.99 TC 1.16 TT 1.37 rs75915166 11 C/A 0.059 1.0239 1.00 CC 1.00 AC 1.02 AA 1.05 rs11820646 11 C/T 0.41 0.9563 0.96 CC 1.04 TC 0.99 TT 0.95 rs12422552 12 G/C 0.26 1.0327 1.02 GG 0.98 CG 1.02 CC 1.05 rs10771399 12 A/G 0.12 0.8629 0.97 AA 1.03 GA 0.89 GG 0.77 rs17356907 12 A/G 0.3 0.9078 0.95 AA 1.06 GA 0.96 GG 0.87 rs1292011 12 A/G 0.42 0.9219 0.94 AA 1.07 GA 0.99 GG 0.91 rs11571833 13 A/T 0.008 1.2609 1.00 AA 1.00 TA 1.26 TT 1.58 rs2236007 14 G/A 0.21 0.9203 0.97 GG 1.03 AG 0.95 AA 0.88 rs999737 14 C/T 0.23 0.9239 0.97 CC 1.04 TC 0.96 TT 0.88 rs2588809 14 C/T 0.16 1.0667 1.02 CC 0.98 TC 1.04 TT 1.11 rs941764 14 A/G 0.34 1.0636 1.04 AA 0.96 GA 1.02 GG 1.08 rs3803662 16 G/A 0.26 1.2257 1.12 GG 0.89 AG 1.09 AA 1.34 rs17817449 16 T/G 0.4 0.9300 0.94 TT 1.06 GT 0.98 GG 0.92 rs11075995 16 A/T 0.241 1.0368 1.02 AA 0.98 TA 1.02 TT 1.06 rs13329835 16 A/G 0.22 1.0758 1.03 AA 0.97 GA 1.04 GG 1.12 rs6504950 17 G/A 0.28 0.9340 0.96 GG 1.04 AG 0.97 AA 0.91 rs527616 18 G/C 0.38 0.9573 0.97 GG 1.03 CG 0.99 CC 0.95 rs1436904 18 T/G 0.4 0.9466 0.96 TT 1.04 GT 0.99 GG 0.94 rs2363956 19 G/T 0.487 1.0264 1.03 GG 0.97 TG 1.00 TT 1.03 rs8170 19 G/A 0.19 1.0314 1.01 GG 0.99 AG 1.02 AA 1.05 rs4808801 19 AIG 035 0.9349 0.95 AA 1.05 GA 0.98 GG 0.92 rs3760982 19 G/A 0.46 1.0553 1.05 GG 0.95 AG 1.00 AA 1.06 rs2284378 20 rs2823093 21 G/A 0.27 0.9274 0.96 GG 1.04 AG 0.96 AA 0.89 rs17879961 22 A/G 0.005 1.3632 1.00 AA 1.00 GA 1.36 GG 1.85 rs132390 22 T/C 0.036 1.1091 1.01 TT 0.99 CT 1.10 CC 1.22 rs6001930 22 T/C 0.11 1.1345 1.03 TT 0.97 CT 1.10 CC 1.25

TABLE 10 African American SNPs (n = 78). Alleles represented as risk/reference (non-risk) (eg for rs616488 A is the risk allele). Risk OR allele Risk SNP Chromosome Alleles frequency Allele μ Adjusted Risk Score rs616488  1 A/G 0.86 1.03 1.05 AA 0.95 AG 0.98 GG 1.01 rs11552449  1 C/T 0.037 0.9 0.99 CC 1.01 CT 0.91 TT 0.82 rs11249433  1 A/G 0.13 0.99 1.00 AA 1.00 AG 0.99 GG 0.98 rs6678914  1 G/A 0.66 1 1.00 GG 1.00 GA 1.00 AA 1.00 rs4245739  1 A/C 0.24 0.97 0.99 AA 1.01 AC 0.98 CC 0.95 rs12710696  2 G/A 0.53 1.06 1.06 GG 0.94 GA 1.00 AA 1.06 rs4849887  2 C/T 0.7 1.16 1.24 CC 0.81 CT 0.94 TT 1.09 rs2016394  2 G/A 0.72 1.05 1.07 GG 0.93 GA 0.98 AA 1.03 rs1550623  2 A/G 0.71 1.1 1.15 AA 0.87 AG 0.96 GG 1.05 rs1045485  2 G/C 0.93 0.99 0.98 GG 1.02 GC 1.01 CC 1.00 rs13387042  2 A/G 0.72 1.12 1.18 AA 0.85 AG 0.95 GG 1.06 rs16857609  2 C/T 0.24 1.17 1.08 CC 0.92 CT 1.08 TT 1.26 rs6762644  3 A/G 0.46 1.05 1.05 AA 0.96 AG 1.00 GG 1.05 rs4973768  3 C/T 0.36 1.04 1.03 CC 0.97 CT 1.01 TT 1.05 rs12493607  3 G/C 0.14 1.04 1.01 GG 0.99 GC 1.03 CC 1.07 rs7696175  4 rs9790517  4 C/T 0.084 0.88 0.98 CC 1.02 CT 0.90 TT 0.79 rs6828523  4 C/A 0.65 1 1.00 CC 1.00 CA 1.00 AA 1.00 rs4415084  5 C/T 0.61 1.1 1.13 CC 0.89 CT 0.98 TT 1.07 rs10069690  5 C/T 0.57 1.13 1.15 CC 0.87 CT 0.98 TT 1.11 rs7726159  5 rs2736108  5 rs10941679  5 A/G 0.21 1.04 1.02 AA 0.98 AG 1.02 GG 1.06 rs889312  5 A/C 0.33 1.07 1.05 AA 0.96 AC 1.02 CC 1.09 rs10472076  5 T/C 0.28 0.95 0.97 TT 1.03 TC 0.98 CC 0.93 rs2067980  5 rs1353747  5 T/G 0.98 1.01 1.02 TT 0.98 TG 0.99 GG 1.00 rs1432679  5 A/G 0.79 1.07 1.11 AA 0.90 AG 0.96 GG 1.03 rs11242675  6 T/C 0.51 1.06 1.06 TT 0.94 TC 1.00 CC 1.06 rs204247  6 A/G 0.34 1.13 1.09 AA 0.92 AG 1.04 GG 1.17 rs17529111  6 A/G 0.075 0.99 1.00 AA 1.00 AG 0.99 GG 0.98 rs2180341  6 rs9485370  6 G/T 0.78 1.13 1.21 GG 0.82 GT 0.93 TT 1.05 rs12662670  6 rs3757318  6 G/A 0.038 1.11 1.01 GG 0.99 GA 1.10 AA 1.22 rs2046210  6 G/A 0.6 0.99 0.99 GG 1.01 GA 1.00 AA 0.99 rs17157903  7 rs720475  7 G/A 0.88 0.99 0.98 GG 1.02 GA 1.01 AA 1.00 rs9693444  8 C/A 0.37 1.06 1.04 CC 0.96 CA 1.01 AA 1.08 rs6472903  8 T/G 0.9 1.02 1.04 TT 0.96 TG 0.98 GG 1.00 rs2943559  8 A/G 0.22 1.07 1.03 AA 0.97 AG 1.04 GG 1.11 rs13281615  8 A/G 0.43 1.06 1.05 AA 0.95 AG 1.01 GG 1.07 rs11780156  8 C/T 0.052 0.84 0.98 CC 1.02 CT 0.85 TT 0.72 rs1011970  9 G/T 0.32 1.06 1.04 GG 0.96 GT 1.02 TT 1.08 rs10759243  9 C/A 0.59 1.02 1.02 CC 0.98 CA 1.00 AA 1.02 rs865686  9 T/G 0.51 1.09 1.09 TT 0.91 TG 1.00 GG 1.09 rs2380205 10 C/T 0.42 0.98 0.98 CC 1.02 CT 1.00 TT 0.98 rs7072776 10 G/A 0.49 1.04 1.04 GG 0.96 GA 1.00 AA 1.04 rs11814448 10 A/C 0.61 1.04 1.05 AA 0.95 AC 0.99 CC 1.03 rs10822013 10 T/C 0.23 1 1.00 TT 1.00 TC 1.00 CC 1.00 rs10995190 10 G/A 0.83 0.98 0.97 GG 1.03 GA 1.01 AA 0.99 rs704010 10 C/T 0.11 0.98 1.00 CC 1.00 CT 0.98 TT 0.96 rs7904519 10 AIG 0.78 1.13 1.21 AA 0.82 AG 0.93 GG 1.05 rs2981579 10 G/A 0.59 1.18 1.22 GG 0.82 GA 0.96 AA 1.14 rs2981582 10 G/A 0.49 1.05 1.05 GG 0.95 GA 1.00 AA 1.05 rs11199914 10 C/T 0.48 0.97 0.97 CC 1.03 CT 1.00 TT 0.97 rs3817198 11 T/C 0.17 0.98 0.99 TT 1.01 TC 0.99 CC 0.97 rs3903072 11 G/T 0.82 0.99 0.98 GG 1.02 GT 1.01 TT 1.00 rs554219 11 C/G 0.22 1 1.00 CC 1.00 CG 1.00 GG 1.00 rs614367 11 G/A 0.13 0.96 0.99 GG 1.01 GA 0.97 AA 0.93 rs78540526 11 rs75915166 11 C/A 0.015 1.44 1.01 CC 0.99 CA 1.42 AA 2.05 rs11820646 11 C/T 0.78 0.98 0.97 CC 1.03 CT 1.01 TT 0.99 rs12422552 12 G/C 0.41 1.02 1.02 GG 0.98 GC 1.00 CC 1.02 rs10771399 12 A/G 0.96 1.19 1.40 AA 0.72 AG 0.85 GG 1.01 rs17356907 12 A/G 0.79 1.02 1.03 AA 0.97 AG 0.99 GG 1.01 rs1292011 12 A/G 0.55 1.03 1.03 AA 0.97 AG 1.00 GG 1.03 rs11571833 13 A/T 0.003 0.95 1.00 AA 1.00 AT 0.95 TT 0.90 rs2236007 14 G/A 0.93 0.9 0.82 GG 1.22 GA 1.09 AA 0.98 rs999737 14 C/T 0.95 1.03 1.06 CC 0.95 CT 0.97 TT 1.00 rs2588809 14 C/T 0.29 1.01 1.01 CC 0.99 CT 1.00 TT 1.01 rs941764 14 A/G 0.7 1.1 1.14 AA 0.87 AG 0.96 GG 1.06 rs3803662 16 G/A 0.51 0.99 0.99 GG 1.01 GA 1.00 AA 0.99 rs17817449 16 T/G 0.6 1.05 1.06 TT 0.94 TG 0.99 GG 1.04 rs11075995 16 A/T 0.18 1.07 1.03 AA 0.98 AT 1.04 TT 1.12 rs13329835 16 A/G 0.63 1.08 1.10 AA 0.91 AG 0.98 GG 1.06 rs6504950 17 G/A 0.65 1.06 1.08 GG 0.93 GA 0.98 AA 1.04 rs527616 18 G/C 0.86 0.98 0.97 GG 1.04 GC 1.01 CC 0.99 rs1436904 18 T/G 0.75 0.98 0.97 TT 1.03 TG 1.01 GG 0.99 rs2363956 19 rs8170 19 G/A 0.19 1.13 1.05 GG 0.95 GA 1.08 AA 1.22 rs4808801 19 A/G 0.33 1.01 1.01 AA 0.99 AG 1.00 GG 1.01 rs3760982 19 G/A 0.47 1 1.00 GG 1.00 GA 1.00 AA 1.00 rs2284378 20 C/T 0.16 1.06 1.02 CC 0.98 CT 1.04 TT 1.10 rs2823093 21 G/A 0.57 1.03 1.03 GG 0.97 GA 1.00 AA 1.03 rs17879961 22 rs132390 22 T/C 0.052 0.88 0.99 TT 1.01 TC 0.89 CC 0.78 rs6001930 22 T/C 0.13 1.02 1.01 TT 0.99 TC 1.01 CC 1.04

TABLE 11 Hispanic SNPs (n = 82). Alleles represented as major/minor (eg for rs616488 A is the common allele and G less common). OR minor allele numbers below 1 means the minor allele is not the risk allele, whereas when above 1 the minor allele is the risk allele. Minor OR allele Minor SNP Chromosome Alleles frequency Allele μ Adjusted Risk Score rs616488  1 A/G 0.33 0.9417 0.96 AA 1.04 GA 0.98 GG 0.92 rs11552449  1 C/T 0.17 1.0810 1.03 CC 0.97 TC 1.05 TT 1.14 rs11249433  1 A/G 0.40 1.0993 1.08 AA 0.93 GA 1.02 GG 1.12 rs6678914  1 G/A 0.414 0.9890 0.99 GG 1.01 AG 1.00 AA 0.99 rs4245739  1 A/C 0.258 1.0291 1.02 AA 0.99 CA 1.01 CC 1.04 rs12710696  2 G/A 0.357 1.0387 1.03 GG 0.97 AG 1.01 AA 1.05 rs4849887  2 C/T 0.098 0.9187 0.98 CC 1.02 TC 0.93 TT 0.86 rs2016394  2 G/A 0.48 0.9504 0.95 GG 1.05 AG 1.00 AA 0.95 rs1550623  2 A/G 0.16 0.9445 0.98 AA 1.02 GA 0.96 GG 0.91 rs1045485  2 G/C 0.13 0.9644 0.99 GG 1.01 CG 0.97 CC 0.94 rs13387042  2 A/G 0.49 0.8794 0.89 AA 1.13 GA 0.99 GG 0.87 rs16857609  2 C/T 0.26 1.0721 1.04 CC 0.96 TC 1.03 TT 1.11 rs6762644  3 A/G 0.4 1.0661 1.05 AA 0.95 GA 1.01 GG 1.08 rs4973768  3 C/T 0.47 1.0938 1.09 CC 0.92 TC 1.00 TT 1.10 rs12493607  3 G/C 0.35 1.0529 1.04 GG 0.96 CG 1.01 CC 1.07 rs7696175  4 T/C 0.38 1.14 1.11 TT 0.90 CT 1.03 CC 1.17 rs9790517  4 C/T 0.23 1.0481 1.02 CC 0.98 TC 1.03 TT 1.07 rs6828523  4 C/A 0.13 0.9056 0.98 CC 1.03 AC 0.93 AA 0.84 rs4415084  5 rs10069690  5 C/T 0.26 1.0242 1.01 CC 0.99 TC 1.01 TT 1.04 rs7726159  5 C/A 0.338 1.0359 1.02 CC 0.98 AC 1.01 AA 1.05 rs2736108  5 C/T 0.292 0.9379 0.96 CC 1.04 TC 0.97 TT 0.91 rs10941679  5 A/G 0.25 1.1198 1.06 AA 0.94 GA 1.06 GG 1.18 rs889312  5 A/C 0.28 1.1176 1.07 AA 0.94 CA 1.05 CC 1.17 rs10472076  5 T/C 0.38 1.0419 1.03 TT 0.97 CT 1.01 CC 1.05 rs2067980  5 G/A 0.16 1 1.00 GG 1.00 AG 1.00 AA 1.00 rs1353747  5 T/G 0.095 0.9213 0.99 TT 1.02 GT 0.94 GG 0.86 rs1432679  5 A/G 0.43 1.0670 1.06 AA 0.94 GA 1.01 GG 1.08 rs11242675  6 T/C 0.39 0.9429 0.96 TT 1.05 CT 0.99 CC 0.93 rs204247  6 A/G 0.43 1.0503 1.04 AA 0.96 GA 1.01 GG 1.06 rs17529111  6 A/G 0.218 1.0457 1.02 AA 0.98 GA 1.03 GG 1.07 rs2180341  6 G/A 0.23 0.9600 0.98 GG 1.02 AG 0.98 AA 0.94 rs9485370  6 rs12662670  6 T/G 0.073 1.1392 1.02 TT 0.98 GT 1.12 GG 1.27 rs3757318  6 rs2046210  6 G/A 0.34 1.0471 1.03 GG 0.97 AG 1.01 AA 1.06 rs17157903  7 T/C 0.09 0.93 0.99 TT 1.01 CT 0.94 CC 0.88 rs720475  7 G/A 0.25 0.9452 0.97 GG 1.03 AG 0.97 AA 0.92 rs9693444  8 C/A 0.32 1.0730 1.05 CC 0.95 AC 1.02 AA 1.10 rs6472903  8 T/G 0.18 0.9124 0.97 Tr 1.03 GT 0.94 GG 0.86 rs2943559  8 A/G 0.07 1.1334 1.02 AA 0.98 GA 1.11 GG 1.26 rs13281615  8 A/G 0.41 1.0950 1.08 AA 0.93 GA 1.01 GG 1.11 rs11780156  8 C/T 0.16 1.0691 1.02 CC 0.98 TC 1.05 TT 1.12 rs1011970  9 G/T 0.17 1.0502 1.02 GG 0.98 TG 1.03 TT 1.08 rs10759243  9 C/A 0.39 1.0542 1.04 CC 0.96 AC 1.01 AA 1.07 rs865686  9 T/G 0.38 0.8985 0.92 TT 1.08 GT 0.97 GG 0.87 rs2380205 10 C/T 0.44 0.9771 0.98 CC 1.02 TC 1.00 TT 0.97 rs7072776 10 G/A 0.29 1.0581 1.03 GG 0.97 AG 1.02 AA 1.08 rs11814448 10 A/C 0.02 1.2180 1.01 AA 0.99 CA 1.21 CC 1.47 rs10822013 10 rs10995190 10 G/A 0.16 0.8563 0.95 GG 1.05 AG 0.90 AA 0.77 rs704010 10 C/T 0.38 1.0699 1.05 CC 0.95 TC 1.02 TT 1.09 rs7904519 10 A/G 0.46 1.0584 1.05 AA 0.95 GA 1.00 GG 1.06 rs2981579 10 G/A 0.4 1.2524 1.21 GG 0.83 AG 1.03 AA 1.29 rs2981582 10 T/C 0.42 1.1900 1.17 TT 0.86 CT 1.02 CC 1.21 rs11199914 10 C/T 0.32 0.9400 0.96 CC 1.04 TC 0.98 TT 0.92 rs3817198 11 T/C 0.31 1.0744 1.05 TT 0.96 CT 1.03 CC 1.10 rs3903072 11 G/T 0.47 0.9442 0.95 GG 1.05 TG 1.00 TT 0.94 rs554219 11 C/G 0.112 1.1238 1.03 CC 0.97 GC 1.09 GG 1.23 rs614367 11 rs78540526 11 C/T 0.032 1.1761 1.01 CC 0.99 TC 1.16 TT 1.37 rs75915166 11 C/A 0.059 1.0239 1.00 CC 1.00 AC 1.02 AA 1.05 rs11820646 11 C/T 0.41 0.9563 0.96 CC 1.04 TC 0.99 TT 0.95 rs12422552 12 G/C 0.26 1.0327 1.02 GG 0.98 CG 1.02 CC 1.05 rs10771399 12 A/G 0.12 0.8629 0.97 AA 1.03 GA 0.89 GG 0.77 rs17356907 12 A/G 0.3 0.9078 0.95 AA 1.06 GA 0.96 GG 0.87 rs1292011 12 A/G 0.42 0.9219 0.94 AA 1.07 GA 0.99 GG 0.91 rs11571833 13 A/T 0.008 1.2609 1.00 AA 1.00 TA 1.26 TT 1.58 rs2236007 14 G/A 0.21 0.9203 0.97 GG 1.03 AG 0.95 AA 0.88 rs999737 14 C/T 0.23 0.9239 0.97 CC 1.04 TC 0.96 TT 0.88 rs2588809 14 C/T 0.16 1.0667 1.02 CC 0.98 TC 1.04 TT 1.11 rs941764 14 A/G 0.34 1.0636 1.04 AA 0.96 GA 1.02 GG 1.08 rs3803662 16 G/A 0.26 1.2257 1.12 GG 0.89 AG 1.09 AA 1.34 rs17817449 16 T/G 0.4 0.9300 0.94 TT 1.06 GT 0.98 GG 0.92 rs11075995 16 A/T 0.241 1.0368 1.02 AA 0.98 TA 1.02 TT 1.06 rs13329835 16 A/G 0.22 1.0758 1.03 AA 0.97 GA 1.04 GG 1.12 rs6504950 17 G/A 0.28 0.9340 0.96 GG 1.04 AG 0.97 AA 0.91 rs527616 18 G/C 0.38 0.9573 0.97 GG 1.03 CG 0.99 CC 0.95 rs1436904 18 T/G 0.4 0.9466 0.96 TT 1.04 GT 0.99 GG 0.94 rs2363956 19 G/T 0.487 1.0264 1.03 GG 0.97 TG 1.00 TT 1.03 rs8170 19 G/A 0.19 1.0314 1.01 GG 0.99 AG 1.02 AA 1.05 rs4808801 19 A/G 0.35 0.9349 0.95 AA 1.05 GA 0.98 GG 0.92 rs3760982 19 G/A 0.46 1.0553 1.05 GG 0.95 AG 1.00 AA 1.06 rs2284378 20 rs2823093 21 G/A 0.27 0.9274 0.96 GG 1.04 AG 0.96 AA 0.89 rs17879961 22 A/G 0.005 1.3632 1.00 AA 1.00 GA 1.36 GG 1.85 rs132390 22 T/C 0.036 1.1091 1.01 TT 0.99 CT 1.10 CC 1.22 rs6001930 22 T/C 0.11 1.1345 1.03 TT 0.97 CT 1.10 CC 1.25

Example 2 Risk Thresholds

Breast cancer risk assessment is important as it allows the identification of women who are at elevated risk who may benefit from either targeted screening or preventative measures (De la Cruz, 2014; Advani and Morena-Aspitia, 2014). Both genetic and environmental factors are thought to play a role in multifactorial susceptibility to breast cancer (Lichtenstein et al., 2000; Mahoney et al., 2008). In order to optimally assess risk, both components are considered together. Currently, breast cancer risk is often assessed by utilizing the National Cancer Institute's (NCI) Breast Cancer Risk Assessment Tool (BCRAT), often referred to as the “Gail Model” (Gail et al., 1989; Costantino et al., 1999; Rockhill et al., 2001). The BCRAT incorporates several risk factors related to personal history and also incorporates some family history information.

The current model takes the information provided by the ordering physician to calculate a Gail score, and combines it with the patient's common genetic markers for breast cancer to produce Integrated Lifetime (Example shown in FIG. 1) and 5-Year patient risk (Example shown in FIG. 2) assessments for breast cancer. It is recommended that a patient receive appropriate genetic or clinical counselling to explain the implications of the test results. American Cancer Society (ACS) guidelines recommend screening breast MRIC and mammography for women at high risk (20% lifetime risk). American Society of Clinical Oncology (ASCO) suggest women at high risk (GAIL index >1.66% for 5-year risk) may be offered an estrogen receptor therapy to reduce their risk.

The current test provides additional important information about a woman's risk of developing breast cancer by assessing genetic information from a cheek cell sample. The test detects SNPs. At least 70 of these distinct genetic locations are analysed (genotyped), each of which has been shown reproducibly to modify an individual's odds of developing breast cancer. The test combines the information from all SNPs in the panel because the scientific validation studies support a simple multiplicative model for combining the SNP risks (Mealiffe et al., 2010).

Example 3 Combination of SNP Risk Scores with Breast Cancer Risk Models

There are several popular breast cancer risk prediction models. These include BOADICEA (Antonio et al., 2008 and 2009) and BRCAPRO (Chen et al., 2004; Mazzola et al., 2014; Parmigianin et al., 1998), both of which are based on pedigree data for breast and ovarian cancer; the Gail Model (BCRAT) (Costatino et al., 1999; Gail et al., 1989), which is based on established risk factors for breast cancer and family history represented by the number of first-degree relatives with breast cancer; and the Tyrer-Cuzick Model (IBIS) (Tyrer et al., 2004), which combines information on familial and personal risk factors for breast cancer. At an individual level, all of these risk prediction models must have good discriminatory accuracy to be able to provide information that is clinically useful to help a woman make decisions on screening or prevention that are tailored to her specific circumstances.

The present inventors tested the ability of a 77 SNP panel to improve the discriminatory accuracy of the Gail, Tyrer-Cuzick, BOADICEA and BRCAPRO models in a Caucasian cohort.

For each risk prediction model, the five-year clinical risk of invasive breast cancer was calculated. For BCRAT, in accordance with the model's design, risk predictions were restricted to women aged 35 years and older. A SNP risk score was calculated using published estimates of the odds ratio (OR) per allele and risk allele frequencies (p) assuming independence of additive risks on the log OR scale. For each SNP, the unsealed population average risks were calculated as 1/μ, OR/μ and OR2/μ for the three genotypes. The SNP risk score was then calculated by multiplying the adjusted risk values for each of the 77 SNPs (Dite et al., 2013). For each risk prediction model, a combined risk score was calculated by multiplying the SNP risk score by the model's predicted five-year risk. Discrimination was measured by calculating the area under the receiver operating characteristic curve (AUC).

Table 12 shows that for each of the four risk prediction models, the combined risk score gave higher discrimination than the risk scores alone.

Example 4 Calculation of Risk

This example is a hypothetical case in which the inventors have assumed that all factors remain constant, except for ethnicity of the woman. In this example the three women (one Caucasian, one African-American and one Hispanic) have the following characteristics—45 year old, age at first period was 12, first child at 26, no first-degree relatives with breast cancer, and have not had any positive breast biopsies.

Table 13 outlines the genotypes of the three women, whereas Table 14 provides details of the risk calculation.

TABLE 12 Area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI) for each of the risk scores. Risk Algorithm AUC (95% CI) Gail (BCRAT) 0.64 (0.60, 0.68) Tyrer-Cuzick (IBIS) 0.57 (0.54, 0.60) BOADICEA 0.66 (0.63, 0.70) BRCAPRO 0.63 (0.60, 0.67) Gail × SNP 0.66 (0.62, 0.70) Tyrer-Cuzick × SNP 0.63 (0.59, 0.66) BOADICEA × SNP 0.69 (0.66, 0.73) BRCAPRO × SNP 0.68 ((0.65, 0.71) 

TABLE 13 Analysis of hypothetical genotypes from three women of different ethnicity and calculation of genetic risk.

TABLE 14 Risk calculations using the genotype scores from Table 13. Combined Gail Gail Combined SNP × 5-Year Lifetime SNP SNP × Lifetime Risk Risk risk 5-Year Risk Risk Caucasian 0.9% 10.6% 5.75 5.175%  60.95% African 0.9% 9.3% 1.09 0.98% 10.14% American Hispanic 0.6% 7.5% 0.67 0.40% 5.03%

The impact of the genotypic risk is evidenced when we multiply the genotypic and clinical risk (Gail Score) together. In the above instance, the Caucasian has their 5-Year risk elevated to 5.175% and would be offered Tamoxifen chemoprevention. She also has her Lifetime risk elevated to 60.95% and would be offered annual MRI screening.

The African American has a genotypic risk score close to 1 and her risk remains close to average (5-year risk=0.985 and lifetime risk=10.14%).

The Hispanic woman has a genotypic risk of 0.67 (i.e., this genotype is protective) and her subsequent 5-year risk is reduced to 0.40% and her lifetime risk reduced to 5.03%.

It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

All publications discussed and/or referenced herein are incorporated herein in their entirety.

Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.

The present application claims priority from AU 2014903898 filed 30 Sep. 2014, the disclosures of which are incorporated herein by reference.

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1. A method for assessing the genetic risk of a human female subject developing breast cancer comprising: a) obtaining from a biological sample derived from the female subject, the identity of alleles present at at least 72 single nucleotide polymorphisms associated with breast cancer, of which at least 67 of the single nucleotide polymorphisms are selected from Table 7, and the remaining single nucleotide polymorphisms are selected from Table 6; b) producing the human female subject's genetic risk assessment by multiplying together an adjusted risk score for each of the at least 72 SNPs, where the adjusted risk score for each of the at least 72 SNPs is 1/μ when two low-risk alleles are present, OR/μ when one low-risk and one high-risk allele are present, OR2/μ when two high-risk alleles are present, and 1 when the genotype is missing for the SNP, where μ=(1−p)2+2p(1−p)OR+p2OR2, wherein OR is the odds ratio of a high risk allele at the given SNP and p is the frequency of the high risk allele in the population to which the human female subject belongs.
 2. The method of claim 1, wherein the biological sample is blood.
 3. The method of claim 1, wherein the biological sample is saliva.
 4. The method of claim 1, wherein the biological sample is a cheek cell sample.
 5. The method of claim 1, wherein the biological sample is urine.
 6. The method of claim 1, wherein the female has not had breast cancer, lobular carcinoma or ductal carcinoma.
 7. The method of claim 1, wherein the female has had a biopsy of the breast.
 8. The method according to claim 1, wherein the results of the genetic risk assessment indicate that the female should be subjected to more frequent screening and/or prophylactic anti-breast cancer therapy.
 9. A system for assessing the genetic risk of a human female subject for developing breast cancer comprising system instructions for performing a genetic risk assessment of the female subject according to claim 1 to obtain the genetic risk of a human female subject developing breast cancer.
 10. A computer implemented method for assessing the genetic risk of a human female subject developing breast cancer, the method operable in a computing system comprising a processor and a memory, the method comprising: receiving data comprising the identity of alleles present at at least 72 single nucleotide polymorphisms associated with breast cancer obtained from a biological sample derived from the female subject, wherein at least 67 of the single nucleotide polymorphisms are selected from Table 7, and the remaining single nucleotide polymorphisms are selected from Table 6; processing the data by multiplying together an adjusted risk score for each of the at least 72 SNPs, where the adjusted risk score for each of the at least 72 SNPs is 1/μ when two low-risk alleles are present, OR/μ when one low-risk and one high-risk allele are present, OR2/g when two high-risk alleles are present, and 1 when the genotype is missing for the SNP, where g=(1−p)2+2p(1−p)OR+p2OR2, wherein OR is the odds ratio of a high risk allele at the given SNP and p is the frequency of the high risk allele in the population to which the human female subject belongs; and outputting the genetic risk of a human female subject developing breast cancer.
 11. The method of claim 10, wherein the biological sample is blood.
 12. The method of claim 10, wherein the biological sample is saliva.
 13. The method of claim 10, wherein the biological sample is a cheek cell sample.
 14. The method of claim 10, wherein the biological sample is urine.
 15. The method of claim 10, wherein the female has not had breast cancer, lobular carcinoma or ductal carcinoma.
 16. The method of claim 10, wherein the female has had a biopsy of the breast.
 17. The method according to claim 10, wherein the results of the genetic risk assessment indicate that the female should be subjected to more frequent screening and/or prophylactic anti-breast cancer therapy. 